a class of solutions of the time dependent reaction diffusion equation for the processes a a to a and a a to oe consideration is given to the treatment of the nonlinear partial differential equation describing the diffusion of an assembly of particles which simultaneously coagulate or annihilate it is shown how a class of solutions may be obtained in terms of the solutions of an ordinary differential equation and detailed application is made to the case of an initially localized particle distribution

an introduction to mathematical models of coagulation fragmentation processes a discrete deterministic mean field approach abstract we summarise the properties and the fundamental mathematical results associated with basic models which describe coagulation and fragmentation processes in a deterministic manner and in which cluster size is a discrete quantity an integer multiple of some basic unit size in particular we discuss smoluchowski s equation for aggregation the becker doring model of simultaneous aggregation and fragmentation and more general models involving coagulation and fragmentation

on the treatment of thermal and quantum annealing by stochastic variational means we consider the control of a diffusion process represented by a stochastic differential equation of the ito type the diffusion process is one of a random walk on the configuration space of a multimodal function and the goal of the control is to determine the optimum variation of the temperature in order to anneal into the global minimum of the function such optimal controls are derived using stochastic variational means by the viscosity solution of a second order hamilton jacobi bellman equation or by minimizing a feynman kac wiener path integral the extension to quantized annealing and the possibilities of a differential game theoretic formulation for extra diffusive annealing are also considered

soccer game optimization with substitute players metaheuristic methods are powerful optimization methods to solve various problems and have been used widely in this paper a player substitution mechanism to enhance the performance of the soccer game optimization sgo is proposed the substitution mechanism refers to the competition concept existing in the evolutionary algorithm the substitute players are continuously updated to keep up with a set of best solutions so far this paper discusses the sensitivity of its parameters as well as assesses the performance of the proposed method using unconstraint continuous problems the proposed method is compared to the sgo without player substitution biogeography based optimization bbo ant colony optimization aco differential evolution de evolutionary strategies es genetic algorithm ga and particle swarm optimization pso the experiment results show that the proposed method performs better than other algorithms used in this paper player substitution mechanism is proposed to enhance the soccer games optimization the proposed method is compared to sgo without substitution the experiment results confirm the proposed method is better we investigate the method sensitivity for unimodal and multimodal functions

developing a fuzzy bicluster regression to estimate heat tolerance in plants by chlorophyll fluorescence this paper presents a straightforward and useful fuzzy regression approach to estimate heat tolerance of plants by chlorophyll fluorescence measurement the chlorophyll fluorescence measurement is an indicator of functional change of photosynthesis and is sensitive to temperature using the fluorescence temperature curves the experimenter may determine the heat tolerance tc of plants by intersections of two linear regression lines however as traditional statistical regression analysis shows the experiment may contain uncertain factors or phenomena such as leaf nature and growth environment which concludes that data may vary among individual plants and different species this research presents a fuzzy bicluster regression fbcr analysis with genetic algorithms which helps derive a fuzzy intersection set and fuzzy heat tolerance of plants in addition to the traditional statistical regression analysis a fuzzy clustering concept and simultaneously optimal determination of data clusters is also developed especially when there are nonlinear inflections in data curves due to the imperative use of linear regression models the traditional regression analysis may become unable to sufficiently model the uncertainties exhibited the fbcr analysis can resolve this problem effectively due to the nonlinear tolerance of the system even in a linear model to demonstrate the fbcr analysis it was applied to estimate the heat tolerance of five plant species the results derived appeared to be more suitable than that of the conventional method the approach may provide a useful means for the experimenters to derive more credible results from their chlorophyll fluorescence temperature data

field theoretic formulation of an epidemic process with immunisation the author describes a model of the spread of an epidemic on a lattice in which sites may be infected by their neighbours after which they recover and are subsequently immune it corresponds to a modification of the directed percolation problem and also to a growth model of clusters the model is analysed using a continuum field theory both within a self consistent approximation and using an epsilon expansion below six transverse dimensions

a new crossover operator for real coded genetic algorithms abstract in this paper a new real coded crossover operator called the laplace crossover lx is proposed lx is used in conjunction with two well known mutation operators namely the makinen periaux and toivanen mutation mptm and non uniform mutation num to define two new generational genetic algorithms lx mptm and lx num respectively these two genetic algorithms are compared with two existing genetic algorithms hx mptm and hx num which comprise of heuristic crossover operator and same two mutation operators a set of 20 test problems available in the global optimization literature is used to test the performance of these four genetic algorithms to judge the performance of the lx operator two kinds of analysis is performed firstly a pair wise comparison is performed between lx mptm and hx mptm and then between lx num and hx num secondly the overall comparison of performances of all the four genetic algorithms is carried out based on a performance index pi the comparative study shows that laplace crossover lx performs quite well and one of the genetic algorithms defined lx mptm outperforms other genetic algorithms

an effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization multiagent systems have been studied and widely used in the field of artificial intelligence and computer science to catalyze computation intelligence in this paper a multiagent evolutionary algorithm called raer based on the era multiagent modeling pattern is proposed where era has the same architecture as swarm including three parts of environment reactive rules and agents raer integrates a novel roulette inversion operator rio proposed in this paper and theoretically proved to conquer the irrationality of the inversion operator io designed by john holland when used for real code stochastic optimization algorithms experiments for numerical optimization of 4 benchmark functions show that the rio operator bears better functioning than io operator and experiments for numerical optimization of 12 benchmark functions are used to examine the performance and scalability of raer along the problem dimensions ranging 20 10000 results indicate that raer outperforms other comparative algorithms significantly also two engineering optimization problems of a stable linear system approximation and a welded beam design are used to examine the applicability of raer results show that raer has better search ability and faster convergence speed especially for the approximation problem rear can find the proper optima belonging to different fixed search areas which is significantly better than other algorithms and shows that raer can search the problem domains more thoroughly than other algorithms hence raer is efficient and practical

global optimizations and tabu search based on memory based on the idea of tabu search that glover et al put forward a new tabu search named memory tabu search mts is proposed for solving the multiple minima problem of continuous functions two convergence theorems which show that mts asymptotically converges to the global optimal solutions in probability one under suitable conditions are given numerical results illustrate that this algorithm is efficient robust and easy to implement

chladni figures for completely free parallelogram plates an analytical study abstract this paper communicates the first comprehensive analytical study of the title problem the ritz method with admissible functions in the form of legendre polynomials is used and is shown to provide rapid convergence and highly economic eigensolutions for a given problem the robustness of the method is demonstrated and proof is given of its validity at extreme skew angles chladni figures are presented for the first six modes of plates with three different aspect ratios and six different skew angles making a total of 108 cases additional results can be inferred for two more plate aspect ratios thus raising the total number of chladni figures to 180 outstanding agreement is obtained between all the analytically derived nodal patterns from this work and the experimental work of other investigators the dependence of frequency on the skew angle the aspect ratio and the poisson ratio is also investigated and extensive results are presented in diagrammatic and graphical format

an intensive restraint topology adaptive snake model and its application in tracking dynamic image sequence the aim of this work is to propose an improved intensive restraint topology adaptive snake model for detecting and tracking target contours with complex topological morphology from dynamic gray scale image sequences the image is discretized by orthogonal equidistant grid lines and snake nodes are constrained to move only from one grid vertex to another along grid lines thus the computation is much simpler than those of other t snakes since complicated calculation of intersection points of snake curve and grid lines is not required topological transformation is implemented through splitting snake nodes as well as automatically detecting and eliminating topological collision an adaptive weighted expansion energy term is introduced into snake s energy function driving the model to pass across the region with non uniform intensity distribution inside the target region the required manual interaction is reduced to only select a random point within the target region in the first frame of the image sequence for subsequent frames the detection result in the previous frame is regarded as the initial model in the current frame through experiments on simulated image data and clinical x ray coronary angiographic sequences the model s topological flexibility and lower computation cost compared with other t snakes are demonstrated also the sensitivity of the model to weights of the energy terms and grid size as well as the initial plan of the model is discussed based on experimental results

a modified real coded genetic algorithm for constrained optimization abstract the performance of a genetic algorithm ga largely depends upon crossover and mutation operators deep and thakur 2007 14 15 proposed a real coded genetic algorithm rcga incorporating laplace crossover lx and power mutation pm operator and shown that the resulting ga named lx pm outperforms many existing rcgas on a large set of scalable test problems of varying difficulty level in this paper lx pm is modified by improving the lx operator the modified lx operator named as bounded exponential crossover bex operator always creates offspring within the variable bounds a new rcga named bex pm incorporating bex and pm operator is proposed the performance of the modified ga is tested against the original algorithm lx pm and three other popular constrained optimization algorithms hx num hx mptm and sbx pol over a test suite containing twenty five constrained optimization problems collected from global optimization literature the performance of all rcgas and quality of solution obtained is compared on the basis of standard criteria used in ga literature the comparative study shows that bex pm performs significantly better than the original algorithm lx pm and outperforms all rcgas considered in this study

interval oriented multi section techniques for global optimization this paper deals with two different optimization techniques to solve the bound constrained nonlinear optimization problems based on division criteria of a prescribed search region finite interval arithmetic and interval ranking in the context of a decision maker s point of view in the proposed techniques two different division criteria are introduced where the accepted region is divided into several distinct subregions and in each subregion the objective function is computed in the form of an interval using interval arithmetic and the subregion containing the best objective value is found by interval ranking the process is continued until the interval width for each variable in the accepted subregion is negligible in this way the global optimal or close to global optimal values of decision variables and the objective function can easily be obtained in the form of an interval with negligible widths both the techniques are applied on several benchmark functions and are compared with the existing analytical and heuristic methods

a tabu search hooke and jeeves algorithm for unconstrained optimization this paper addresses the problem of finding the global minimum of a nonconvex function a new hybrid algorithm for this problem based on tabu search is developed it is hybrid in the sense that search directions are generated using tabu search strategy and then they are used in an optimization algorithm the algorithm is tested on some standard test functions and its performance is compared with existing algorithms computational results show that the proposed algorithm is very efficient and robust

new stopping criterion for genetic algorithms abstract genetic algorithms have been successfully applied in a wide variety of problems although widely used there are few theoretical guidelines for determining when to terminate the search one result by aytug and koehler provides a loose bound on the number of ga generations needed to see all populations and hence an optimal solution with a specified probability in this paper we derive a tighter bound this new bound is on the number of iterations required to achieve a level of confidence to guarantee that a genetic algorithm has seen all strings and hence an optimal solution

the continuous coagulation fragmentation equations with diffusion existence of global weak solutions to the continuous coagulation fragmentation equations with diffusion is investigated when the kinetic coefficients satisfy a detailed balance condition or the coagulation coefficient enjoys a monotonicity condition our approach relies on weak and strong compactness methods in l 1 in the spirit of the diperna lions theory for the boltzmann equation under the detailed balance condition the large time behaviour is also studied

genetic algorithms for continuous optimization problems a concept of parameter space size adjustment the concept of parameter space size adjustment is proposed in order to enable successful application of genetic algorithms to continuous optimization problems performance of genetic algorithms with six different combinations of selection and reproduction mechanisms with and without parameter space size adjustment were severely tested on eleven multiminima test functions an algorithm with the best performance was employed for the determination of the model parameters of the optical constants of pt ni and cr

an intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms time series observed in real world is often nonlinear even chaotic however observed data is often contaminated by noise of various types to effectively extract desired information from observed data it is vital to preprocess data to reduce noise for both the analysis of dynamical systems and many potential applications of these systems in this paper we present a noise reduction approach to the problem of additive source separation characterized by wide band power spectra when one of the sources is chaotic the algorithm is based on a center based genetic algorithm cbga in lifting wavelet framework in which the cbga is used for threshold optimization this method intelligently adapts itself to various types of noise and it weighs preservation of dynamics and denoising through signal to noise ratio snr and root mean square error rmse computer simulations show that the approach is very effective in diminishing different kinds of noise and performs better in terms of visual quality as well as quantitative metrics than existing algorithms

enhancing the performance of differential evolution using orthogonal design method differential evolution de is a simple and efficient global optimization algorithm it has been successfully applied to solve a wide range of real world optimization problems however de has been shown to have certain weaknesses especially if the global optimum should be located using a limited number of function evaluations nfes in this paper we incorporate the orthogonal design method into de to accelerate its convergence rate the orthogonal design method is not only to be used to generate the initial population but also to be applied to design the crossover operator in addition two models of de method are investigated moreover the self adaptive parameter control is employed to avoid tuning the parameters of de experiments have been conducted on 25 problems of diverse complexities and the results indicate that our approach is able to find the optimal or close to optimal solutions in all cases compared with other state of the art evolutionary algorithms eas our approach performs better or at least comparably in terms of the quality and stability of the final solutions

artificial immune multi objective sar image segmentation with fused complementary features artificial immune systems ais are the computational systems inspired by the principles and processes of the vertebrate immune system ais based algorithms typically mimic the human immune system s characteristics of learning and adaptability to solve some complicated problems here an artificial immune multi objective optimization framework is formulated and applied to synthetic aperture radar sar image segmentation the important innovations of the framework are listed as follows 1 an efficient and robust immune multi objective optimization algorithm is proposed which has the features of adaptive rank clones and diversity maintenance by k nearest neighbor list 2 besides two conflicting fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and 3 moreover an effective fused feature set for texture representation and discrimination is constructed and researched which utilizes both the gabor filter s ability to precisely extract texture features in low and mid frequency components and the gray level co occurrence probability s glcp ability to measure information in high frequency two experiments with synthetic texture images and sar images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms fuzzy c means fcm single objective genetic algorithm soga self organizing map som wavelet domain hidden markov models hmtseg and spectral clustering ensemble sce experimental results show the proposed framework has obtained the better performance in segmenting sar images than other five algorithms and behaves insensitive to the speckle noise

the becker doring cluster equations basic properties and asymptotic behaviour of solutions existence and uniqueness results are established for solutions to the becker doring cluster equations the density is shown to be a conserved quantity under hypotheses applying to a model of a quenched binary alloy the asymptotic behaviour of solutions with rapidly decaying initial data is determined denoting the set of equilibrium solutions byc 0 s the principal result is that if the initial density 0 s then the solution converges strongly toc o while if 0 s the solution converges weak toc s in the latter case the excess density 0 s corresponds to the formation of larger and larger clusters i e condensation the main tools for studying the asymptotic behaviour are the use of a lyapunov function with desirable continuity properties obtained from a known lyapunov function by the addition of a special multiple of the density and a maximum principle for solutions

a real coded genetic algorithm with a direction based crossover operator in this paper we develop a parallel structured real coded genetic algorithm rcga named the rga rdd for numerical optimization technically the proposed rga rdd integrates three specially designed evolutionary operators the ranking selection rs direction based crossover dbx and the dynamic random mutation drm as a whole to mimic a specific evolutionary process unlike the conventional rcgas that perform evolutionary operators in a series framework the rga rdd embeds a coordinator in the inner parallel loop to organize the operations of the dbx and drm so that a higher possibility of locating the global optimum is ensured besides based on the results of a systematic parametric analysis we provide a parameter selection guideline for the settings of the proposed rga rdd furthermore a data driven optimization scheme which incorporates the uniform design for design of experiments and a shape tunable neural network for auxiliary decision support is applied to search for an optimal set of the algorithm parameters the effectiveness and applicability of the proposed rga rdd are demonstrated through a variety of benchmarked optimization problems followed by comprehensive comparisons with some existing state of the art evolutionary algorithms extensive simulation results reveal that the performance of the proposed rga rdd is superior to comparative methods in locating the global optimum for real parameter optimization problems especially for unsolved multimodal and high dimensional hybrid functions

a new approach to the modelling of distributed non self adjoint systems abstract this paper is concerned with the modelling of distributed non self adjoint systems such as damped structures in using the method of weighted residuals to produce an approximate solution to the eigenvalue problem the displacement of a non self adjoint structure is ordinarily represented by a linear combination of comparison functions i e functions that satisfy all the boundary conditions for many problems comparison functions are difficult to generate so that the question arises as to an alternative approach clearly solutions in terms of comparison functions satisfy the boundary conditions exactly but experience errors in the differential equation the new approach consists of constructing an approximate solution to the eigenvalue problem from the field of quasi comparison functions defined as linear combinations of admissible functions capable of satisfying all the boundary conditions of the problem the newly developed quasi comparison functions have been used with considerable success for self adjoint structures this paper demonstrates that the same remarkable convergence properties characterize approximate solutions in terms of quasi comparison functions for non self adjoint systems

a directed genetic algorithm for global optimization within the framework of real coded genetic algorithms this paper proposes a directed genetic algorithm dga that introduces a directed crossover operator and a directed mutation operator the operation schemes of these operators borrow from the reflection and the expansion search mode of the nelder mead s simplex method first the taguchi method is employed to study the influence analysis of the parameters in the dga the results show that the parameters in the dga have strong robustness for solving the global optimal solution then several strategies are proposed to enhance the solution accuracy capability of the dga all of the strategies are applied to a set of 30 100 dimensional benchmark functions to prove their superiority over several genetic algorithms finally a cantilevered beam design problem with constrained conditions is used as a practical structural optimization example for demonstrating the very good performance of the proposed method

a new hybrid method for solving global optimization problem abstract in this paper we present a new hybrid method called the sasp method the purpose of this method is the hybridization of the simulated annealing sa with the descent method where we estimate the gradient using simultaneous perturbation firstly the new hybrid method finds a local minimum using the descent method then sa is executed in order to escape from the currently discovered local minimum to a better one from which the descent method restarts a new local search and so on until convergence the new hybrid method can be widely applied to a class of global optimization problems for continuous functions with constraints experiments on 30 benchmark functions including high dimensional functions show that the new method is able to find near optimal solutions efficiently in addition its performance as a viable optimization method is demonstrated by comparing it with other existing algorithms numerical results improve the robustness and efficiency of the method presented

weak solutions to the cauchy problem for the diffusive discrete coagulation fragmentation system abstract the initial value problem for the discrete coagulation fragmentation system with diffusion is studied this is an infinite countable system of reaction diffusion equations describing coagulation and fragmentation of discrete clusters moving by spatial diffusion in all space r d the model considered in this work is a generalization of smoluchowski s discrete coagulation equations existence of global in time weak solutions to the cauchy problem is proved under natural assumptions on initial data for unbounded coagulation and fragmentation coefficients this work extends existence theory for this system from the case of clusters distribution on bounded domain subject to no flux boundary condition to the case of all r d

modifications of real code genetic algorithm for global optimization a series of modifications to the real coded genetic algorithm for the task of locating the global minimum of multidimensional functions are presented here these modifications include a new stopping rule a novel mutation mechanism and a periodically application of a local search procedure the proposed modifications are tested on a series of optimization problems and the results are reported

asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects global minimization via monte carlo the asymptotic behavior of the systems x n 1 x n a n b x n xi n a n sigma x n psi n and dy bar b y dt sqrt a t sigma y dw is studied where psi n is i i d gaussian xi n is a correlated bounded sequence of random variables and a n approx a 0 log a 1 n without xi n such algorithms are versions of the simulated annealing method for global optimization when the objective function values can only be sampled via monte carlo the discrete algorithm is a combination of stochastic approximation and simulated annealing our forms are appropriate the psi n are the annealing variables and xi n is the sampling noise for large a 0 a full asymptotic analysis is presented via the theory of large deviations mean escape time after arbitrary time n from neighborhoods of stable sets of the algorithm mean transition times after arbitrary time n from a neighborhood of one stable set to another a

natural frequencies of rectangular plates using a set of static beam functions in rayleigh ritz method abstract a fast converging series consisting of a set of static beam functions which is a combination of sine series and polynomials is developed and these functions are used as the basis functions in the rayleigh ritz method to study the vibrational characteristics of thin isotropic rectangular plates in applications a relatively small number of functions are required and consequently a modest amount of computation is necessary the approach is very simple and straightforward some numerical results are given for rectangular plates with various aspects ratios and boundary conditions good accuracy and fast convergence of the method are demonstrated by comparison with available results complicating factors orthotropic properties in plane forces etc can also be taken into account without formal difficulties

diffusion for global optimization in r n we seek a global minimum of u mathbb r n to mathbb r the solution to d dt x t nabla u x t will find local minima using the idea of simulated annealing we consider th

asymptotic behaviour of solutions to the diffusive fragmentation coagulation system abstract we study the time asymptotics of solutions to the diffusive fragmentationdashcoagulation system which we derived in a previous work j f collet f poupaud transp theory and stat phys 25 3 5 1996 503 513 in the case where a detailed balance equilibrium exists we derive convergence to such a state by using a careful analysis of the free energy dissipation rate of the system in the case where no detailed balance equilibrium exists we show the existence of an attractor by using classical energy inequalities and some estimates on higher moments of the solution

free vibration of a cantilevered beam with multiple steps comparison of several theoretical methods with experiment abstract the flexural free vibration of a cantilevered beam with multiple cross section steps is investigated theoretically and experimentally experimental results are compared against euler bernoulli beam theory solutions from rayleigh ritz and component modal analyses as well as finite element results using the commercial package ansys finite elements are also used to investigate a timoshenko beam a two dimensional shell and a three dimensional solid element model a detectable difference in the first in plane bending natural frequency is noted between the beam theory results and those of the higher dimensional finite element models and experimental observation the convergence of the several theoretical approaches and their effectiveness as analysis and design methods for multiple stepped beams are also discussed

spatial fluctuations in reaction diffusion systems a model for exponential growth the spatial fluctuations in an exactly soluble model for the irreversible aggregation of clusters are treated the model is characterized byrate constants k ij i j for the clustering of ani and aj mer anddiffusion constants d j d it is assumed thatd 1 reaction limited aggregation explicit expressions for the correlation functions at equal and at different times are calculated the equal time correlation functions show scaling behavior in the scaling limit the correlation length remains finite ast and the fluctuations becomelarge at large times t t d inany dimension the crossover timet d at which the mean field theory smoluchowski s equation breaks down is given byt d ind these exact results imply that the upper critical dimension of this model isd c and hence that there isno unique upper critical dimension in models for the irreversible aggregation of clusters

hybridizing harmony search algorithm with different mutation operators for continuous problems abstract harmony search hs is a recent ea inspired by musical improvisation process to seek a pleasing harmony mutation is a vital component used in evolutionary algorithms ea where a value in the population is randomly selected to be altered to improve the evolution process the original hs algorithm applies an operation similar to mutation during the random consideration operator during random selection operator a value within the range of the decision variable is selected randomly to explore different areas in the search space this paper aims at experimentally evaluating the performance of hs algorithm after replacing the random consideration operator in the original hs with five different mutation methods the different variations of hs are experimented on standard benchmark functions in terms of final obtained solution and convergence speed the results show that using polynomial mutation improves the performance of the hs algorithm for most of the used functions

coagulation diffusion systems derivation and existence of solutions for the diffuse interface structure equations abstract this paper considers an infinite system of partial differential equations the coagulation diffusion equations which add spatial diffusion to the classical coagulation equations the main emphasis is placed on deriving an infinite system of ordinary differential equations which described the structured interface between reacting coagulation and dilute concentration existence of solutions to interfacial equaitons is proven under spatial boundary conditions

on the benefits of laplace samples in solving a rare event problem using cross entropy method the convergence quality of the cross entropy ce optimizer relies critically on the mechanism meant for randomly generating data samples in agreement with the inference drawn in the earlier works the fast simulated annealing fsa and fast evolutionary programming fep since tracing a near global optimum embedded on a nonconvex search space can be viewed as a rare event problem a ce algorithm constructed using a long tailed distribution is intuitively attractive for effectively exploring the optimization landscape based on this supposition a set of ce algorithms employing the cauchy logistic and laplace distributions are experimentally validated in a wide range of optimization functions which are shifted rotated expanded and or composed characterized by convex unimodal discontinuous noisy and multimodal fitness landscapes the laplace distribution has been demonstrated to be more suitable for the ce optimization since the samples drawn have jump lengths long enough to elude local optima and short enough to preserve sufficient candidates in the global optimum neighborhood besides a theoretical analysis has been carried out to understand the following i benefits offered by the long tailed distributions towards evasion of local optima ii link between the variation in scale parameter estimate and the probability of producing candidate solutions arbitrarily close to the global optimum

real coded genetic algorithm with uniform random local search genetic algorithms are efficient global optimizers but they are weak in performing fine grained local searches in this paper the local search capability of genetic algorithm is improved by hybridizing real coded genetic algorithm with uniform random local search to form a hybrid real coded genetic algorithm termed rcgau the incorporated local technique is applied to all newly created offspring so that each offspring solution is given the opportunity to effectively search its local neighborhood for the best local optimum numerical experiments show that the performance of rcga is remarkably improved by the uniform random local search technique

convergence of the classical rayleigh ritz method and the finite element method it is demonstrated in this paper that convergence of the classical rayleigh ritz method can be vastly improved by introducing a new class of admissible functions called quasi comparison functions

particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi threshold image segmentation abstract particle swarm optimization pso algorithm simulates social behavior among individuals or particles flying through multidimensional search space for enhancing the local search ability of pso and guiding the search a region that had most number of the particles was defined and analyzed in detail inspired by the ecological behavior we presented a pso algorithm with intermediate disturbance searching strategy idpso which enhances the global search ability of particles and increases their convergence rates the experimental results on comparing the idpso to ten known pso variants on 16 benchmark problems demonstrated the effectiveness of the proposed algorithm furthermore we applied the idpso algorithm to multilevel image segmentation problem for shortening the computational time experimental results of the new algorithm on a variety of images showed that it can effectively segment an image faster

a hybrid genetic algorithm for a class of global optimization problems with box constraints in this paper a new hybrid genetic algorithm is proposed which combines the genetic algorithm with hill climbing search steps differently from some former algorithms the new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints finally numerical examples show that this algorithm can yield the global optimum with high efficiency

global optimization and simulated annealing in this paper we are concerned with global optimization which can be defined as the problem of finding points on a bounded subset of n in which some real valued functionf assumes its optimal maximal or minimal value we present a stochastic approach which is based on the simulated annealing algorithm the approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems the mathematical formulation is extended to continuous optimization problems and we prove asymptotic convergence to the set of global optima furthermore we discuss an implementation of the algorithm and compare its performance with other well known algorithms the performance evaluation is carried out for a standard set of test functions from the literature

a new mutation operator for real coded genetic algorithms abstract in this paper a new mutation operator called power mutation pm is introduced for real coded genetic algorithms rcga the performance of pm is compared with two other existing real coded mutation operators taken from literature namely non uniform mutation num and makinen periaux and toivanen mutation mptm using the various combinations of two crossovers laplace crossover kusum deep manoj thakur a new crossover operator for real coded genetic algorithms applied mathematics and computations accepted for publication doi 10 1016 j amc 2006 10 047 and heuristic crossover z michalewicz genetic algorithms data structures evolution programs springer verlag new york 1992 a h wright genetic algorithms for real parameter optimization in g j e rawlins ed foundations of genetic algorithms i morgan kaufmann san mateo 1991 pp 205 218 and three mutation operators the newly defined mutation in this paper pm num and mptm six generational real coded gas are compared on a set of 20 benchmark global optimization test problems various performance criterion are used to judge the efficiency accuracy and reliability of all the rcgas the results show that the rcga using the proposed power mutation when used in conjunction with the earlier defined laplace crossover outperforms all other gas considered in this study

a derivative free variant called dfsa of dekkers and aarts continuous simulated annealing algorithm abstract we propose a derivative free implementation of dekkers and aarts continuous simulated annealing sa essential in dfsa is the random direction local technique that we introduce we study the convergence properties of the resulting algorithm and test its performance on a set of 50 problems numerical results are presented which show the robustness of the algorithm numerical comparisons with sa two derivative free simulated annealing algorithms and three population based global optimization algorithms show that the derivative free sa dfsa offers a reasonable alternative to some recent global optimization algorithms especially for problems requiring a direct search type algorithm

diffusions for global optimizations we seek a global minimum of u 0 1 n to r the solution to d dt x t nabla u x t will find local minima the solution to dx t nabla u x t dt sqrt 2t dw t where w is standard n dimensional brownian motion and the boundaries are reflecting will concentrate near the global minima of u at least when temperature t is small the equilibrium distribution for x t is gibbs with density pi t x alpha exp u x t this suggests setting t t t downarrow 0 to find the global minima of u we give conditions on u x and t t such that the solution to dx t nabla u x t dt sqrt 2t dw t converges weakly to a distribution concentrated on the global minima of u

disturbed exploitation compact differential evolution for limited memory optimization problems this paper proposes a novel and unconventional memetic computing approach for solving continuous optimization problems characterized by memory limitations the proposed algorithm unlike employing an explorative evolutionary framework and a set of local search algorithms employs multiple exploitative search within the main framework and performs a multiple step global search by means of a randomized perturbation of the virtual population corresponding to a periodical randomization of the search for the exploitative operators the proposed memetic computing approach is based on a populationless compact evolutionary framework which instead of processing a population of solutions handles its statistical model this evolutionary framework is based on a differential evolution which cooperatively employs two exploitative search operators the first is based on a standard differential evolution mutation and exponential crossover and the second is the trigonometric mutation these two search operators have an exploitative action on the algorithmic framework and thus contribute to the rapid convergence of the virtual population towards promising candidate solutions the action of these search operators is counterbalanced by a periodical stochastic perturbation of the virtual population which has the role of disturbing the excessively exploitative action of the framework and thus inhibits its premature convergence the proposed algorithm namely disturbed exploitation compact differential evolution is a simple and memory wise cheap structure that makes use of the memetic computing paradigm in order to solve complex optimization problems the proposed approach has been tested on a set of various test problems and compared with state of the art compact algorithms and with some modern population based meta heuristics numerical results show that disturbed exploitation compact differential evolution significantly outperforms all the other compact algorithms present in literature and reaches a competitive performance with respect to modern population algorithms including some memetic approaches and complex modern differential evolution based algorithms in order to show the potential of the proposed approach in real world applications disturbed exploitation compact differential evolution has been implemented for performing the control of a space robot by simulating the implementation within the robot micro controller numerical results show the superiority of the proposed algorithm with respect to other modern compact algorithms present in literature

mpso median oriented particle swarm optimization particle swarm optimization pso is a bio inspired optimization algorithm which has been empirically demonstrated to perform well on many optimization problems however it has two main weaknesses which have restricted the wider applications of pso the algorithm can easily get trapped in the local optima and has slow convergence speed therefore improvement and or elimination of these disadvantages are the most important objective in pso research in this paper we propose median oriented particle swarm optimization mpso to carry out a global search over entire search space with accelerating convergence speed and avoiding local optima the median position of particles and the worst and median fitness values of the swarm are incorporated in the standard pso to achieve the mentioned goals the proposed algorithm is evaluated on 20 unimodal multimodal rotated and shifted high dimensional benchmark functions and the results are compared with some well known pso algorithms in the literature the results show that mpso substantially enhances the performance of the pso paradigm in terms of convergence speed and finds global or good near global optimal in the functions

quasi random initial population for genetic algorithms the selection of the initial population in a population based heuristic optimizationmethod is important since it affects the search for several iterations and often has an influence on the final solution if no a priori information about the optima is available the initial population is often selected randomly using pseudorandom numbers usually however it is more important that the points are as evenly distributed as possible than that they imitate random points in this paper we study the use of quasi random sequences in the initial population of a genetic algorithm sample points in a quasi random sequence are designed to have good distribution properties here a modified genetic algorithm using quasi random sequences in the initial population is tested by solving a large number of continuous benchmark problems from the literature the numerical results of two implementations of genetic algorithms using different quasi random sequences are compared to those of a traditional implementation using pseudorandom numbers the results obtained are promising

a simulated annealing driven multi start algorithm for bound constrained global optimization a derivative free simulated annealing driven multi start algorithm for continuous global optimization is presented we first propose a trial point generation scheme in continuous simulated annealing which eliminates the need for the gradient based trial point generation we then suitably embed the multi start procedure within the simulated annealing algorithm we modify the derivative free pattern search method and use it as the local search in the multi start procedure we study the convergence properties of the algorithm and test its performance on a set of 50 problems numerical results are presented which show the robustness of the algorithm numerical comparisons with a gradient based simulated annealing algorithm and three population based global optimization algorithms show that the new algorithm could offer a reasonable alternative to many currently available global optimization algorithms specially for problems requiring direct search type algorithm

a common tabu search algorithm for the global optimization of engineering problems abstract a novel common tabu algorithm for global optimizations of engineering problems is presented the robustness and efficiency of the presented method are evaluated by using standard mathematical functions and by solving a practical engineering problem the numerical results show that the proposed method is i superior to the conventional tabu search algorithm in robustness and ii superior to the simulated annealing algorithm in efficiency

stochastic global optimization methods part 1 clustering methods in this stochastic approach to global optimization clustering techniques are applied to identify local minima of a real valued objective function that are potentially global three different methods of this type are described their accuracy and efficiency are analyzed in detail

stochastic global optimization methods part 11 multi level methods in part ii of our paper two stochastic methods for global optimization are described that with probability 1 find all relevant local minima of the objective function with the smallest possible number of local searches the computational performance of these methods is examined both analytically and empirically

studies of multi start clustering for global optimization global multi modal optimization problems arise in many engineering applications owing to the existence of multiple minima it is a challenge to solve the multi modal optimization problem and to identify the global minimum especially if efficiency is a concern in this paper variants of the multi start with clustering strategy are developed and studied for identifying multiple local minima in nonlinear global optimization problems the study considers the sampling procedure the use of hessian information in forming clusters the technique for cluster analysis and the local search procedure variations of multi start with clustering are applied to 15 multi modal problems a comparative study focuses on the overall search effectiveness in terms of the number of local searches performed local minima found and required function evaluations the performance of these multi start clustering algorithms ranges from very efficient to very robust copyright 2002 john wiley sons ltd

vibration analysis of rectangular plates with general elastic boundary supports abstract in this investigation the rayleigh ritz method is used to determine the modal characteristics of a rectangular plate with general elastic supports alone its edges each of the admissible functions here is composed of a trigonometric function and an arbitrary continuous function that is introduced to ensure the sufficient smoothness of the so called residual displacement function at the edges as a result a drastic improvement of the convergence can be expected of the solution expressed as a series expansion in terms of the admissible functions perhaps more importantly this study has developed a general approach for deriving a complete set of admissible functions that can be universally applied to various boundary conditions several numerical examples are given to demonstrate the accuracy and convergence of the current solution

a dynamic clustering based differential evolution algorithm for global optimization a dynamic clustering based differential evolution algorithm cde for global optimization is proposed to improve the performance of the differential evolution de algorithm with population evolution cde algorithm gradually changes from exploring promising areas at the early stages to exploiting solution with high precision at the later stages experiments on 28 benchmark problems including 13 high dimensional functions show that the new method is able to find near optimal solutions efficiently compared with other existing algorithms cde improves solution accuracy with less computational effort

an efficient algorithm for large scale global optimization of continuous functions a fast descent algorithm resorting to a stretching function technique and built on one hybrid method grsa which combines simulated annealing sa algorithm and gradient based methods for large scale global optimizations is proposed unlike the previously proposed method in which the original objective functions remain unchanged during the whole course of optimization the new method firstly constructs an auxiliary function on one local minimizer obtained by gradient based methods and then sa is executed on this constructed auxiliary function instead of on the original objective function in order that we can improve the jumping ability of sa algorithm to escape from the currently discovered local minimum to a better one from which the gradient based methods restart a new local search the above procedure is repeated until a global minimum is detected in addition corresponding to the adopted stretching technique a new next trial point generating scheme is designed it is verified by simulation especially on large scale problems that the convergence speed is greatly accelerated which is its main difference from many other reported methods that mostly cope with functions with less than 50 variables and does not apply to large scale optimization problems furthermore the new algorithm functions as a global optimization procedure with a high success probability and high solution precision

subset selection in multiple linear regression models a hybrid of genetic and simulated annealing algorithms the question of variable selection in a multiple linear regression model is a major open research topic in statistics the subset selection problem in multiple linear regression deals with the selection of a minimal subset of input variables without loss of explanatory power in this paper we adapt the genetic and simulated annealing algorithms for variable selection in multiple linear regression the performance of this hybrid heuristic method is compared to those obtained by forward selection backward elimination and classical genetic algorithm search a comparative analysis on the literature data sets and simulation data shows that our hybrid heuristic method may suggest efficient alternative to traditional subset selection methods for the variable selection problem in multiple linear regression models

geometricity of genetic operators for real coded representation we investigate geometricity of operators for real coded genetic algorithms geometric operator is a representation independent generalization of the class of traditional mask based operators for binary strings it is defined as a function of the distance of the search space seen as a metric space although the real coded representation allows for a very familiar notion of distance namely the euclidean distance there are also other distances suiting it such as minkowski distances additionally topological transformations of the real space give rise to further notions of distance in this paper we study geometric crossover operators associated with minkowski spaces in a formal and general setting and show that some pre existing genetic operators for the real coded representation are geometric although geometric crossover operators defined in the plane are intuitive operators in higher dimensional spaces they are not trivial to understand and implement we derive formally algorithms to implement these geometric crossover operators in the generic n dimensional case we compare the performance of the presented operators with existing ones on test functions which are commonly used in the literature

renormalisation of fluctuation effects in the a a to a reaction the field theoretical description of the a a to a reaction is exactly renormalised to all orders in perturbation theory it is confirmed that it belong to the same universality class as the a a to oe reaction and that the concentration decay like t d 2 for space dimensionality d smaller than two and like ln t t for d 2 are exact results

field theoretic approach to a source enhanced aggregation process a diffusion controlled cluster cluster aggregation system with a source is analysed using the fock space formalism and diagrammatic perturbation technique the effective coagulation kernel and equation for low dimensional cases d or 2 are derived the asymptotic size distribution obeying a power law is obtained for any dimension the application to the aggregation process of particles undergoing levy flight is discussed through the analysis in this paper the author clarifies a relation between critical phenomena and a source enhanced aggregation

a real coded genetic algorithm for solving integer and mixed integer optimization problems in this paper a real coded genetic algorithm named mi lxpm is proposed for solving integer and mixed integer constrained optimization problems the proposed algorithm is a suitably modified and extended version of the real coded genetic algorithm lxpm of deep and thakur k deep m thakur a new crossover operator for real coded genetic algorithms applied mathematics and computation 188 2007 895 912 k deep m thakur a new mutation operator for real coded genetic algorithms applied mathematics and computation 193 2007 211 230 the algorithm incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints performance of the algorithm is tested on a set of twenty test problems selected from different sources in literature and compared with the performance of an earlier application of genetic algorithm and also with random search based algorithm rst2anu incorporating annealing concept the proposed mi lxpm outperforms both the algorithms in most of the cases which are considered

investigating a hybrid simulated annealing and local search algorithm for constrained optimization constrained optimization problems cop often take place in many practical applications such as kinematics chemical process optimization power systems and so on these problems are challenging in terms of identifying feasible solutions when constraints are non linear and non convex therefore finding the location of the global optimum in the non convex cop is more difficult as compared to non convex bound constrained global optimization problems this paper proposes a hybrid simulated annealing method hsa for solving the general cop hsa has features that address both feasibility and optimality issues and here it is supported by a local search procedure feasible sequential quadratic programming fsqp we develop two versions of hsa the first version hsap incorporates penalty methods for constraint handling and the second one hsad eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently numerical experiments show that the second version is more reliable in the worst case performance

iterative two step genetic algorithm based method for efficient polynomial b spline surface reconstruction surface reconstruction is a very challenging problem arising in a wide variety of applications such as cad design data visualization virtual reality medical imaging computer animation reverse engineering and so on given partial information about an unknown surface its goal is to construct to the extent possible a compact representation of the surface model in most cases available information about the surface consists of a dense set of either organized or scattered 3d data points obtained by using scanner devices a today s prevalent technology in many reverse engineering applications in such a case surface reconstruction consists of two main stages 1 surface parameterization and 2 surface fitting both tasks are critical in order to recover surface geometry and topology and to obtain a proper fitting to data points they are also pretty troublesome leading to a high dimensional nonlinear optimization problem in this context present paper introduces a new method for surface reconstruction from clouds of noisy 3d data points our method applies the genetic algorithm paradigm iteratively to fit a given cloud of data points by using strictly polynomial b spline surfaces genetic algorithms are applied in two steps the first one determines the parametric values of data points the later computes surface knot vectors then the fitting surface is calculated by least squares through either svd singular value decomposition or lu methods the method yields very accurate results even for surfaces with singularities concavities complicated shapes or nonzero genus six examples including open semi closed and closed surfaces with singular points illustrate the good performance of our approach our experiments show that our proposal outperforms all previous approaches in terms of accuracy and flexibility

fluctuations in reaction diffusion systems a new exactly soluble growth model the method of compounding moments devised by van kampen is used to study the spatial fluctuations in a model describing the irreversible formation of clusters the reaction and diffusion constants in this model are chosenindependent of the cluster sizes for a monodisperse initial distribution explicit expressions are calculated for the equal time and two time correlation functions of the concentrations ofm andn mers for general initial conditions the fluctuations in the mass density are considered and a scaling theory is presented for the fluctuations at large times extensions to more general models are discussed

a stochastic approach to global optimization of nonlinear programming problem with many equality constraints many practical problems often lead to large nonconvex nonlinear programming problems that have many equality constraints the global optimization algorithms of these problems have received much attention over the last few years generally stochastic algorithms are suitable for these problems but not efficient when there are too many equality constraints therefore a global optimization algorithm for solving these problems is proposed in this paper the new algorithm based on a feasible set strategy uses a stochastic algorithm and a deterministic local algorithm the convergence of the algorithm is analyzed this algorithm is applied to practical problem and the numerical results illustrate the accuracy and efficiency of the algorithm

inclusion principle for the rayleigh ritz based substructure synthesis according to this substructure synthesis the motion of every substructure is modeled in terms of quasicomparison functions which are linear combinations of admissible functions capable of satisfying all the boundary conditions

probabilistic optimization for conceptual rainfall runoff models a comparison of the shuffled complex evolution and simulated annealing algorithms automatic optimization algorithms are used routinely to calibrate conceptual rainfall runoff crr models the goal of calibration is to estimate a feasible and unique global set of parameter estimates that best fit the observed runoff data most if not all optimization algorithms have difficulty in locating the global optimum because of response surfaces that contain multiple local optima with regions of attraction of differing size discontinuities and long ridges and valleys extensive research has been undertaken to develop efficient and robust global optimization algorithms over the last 10 years this study compares the performance of two probabilistic global optimization methods the shuffled complex evolution algorithm sce ua and the three phase simulated annealing algorithm sa sx both algorithms are used to calibrate two parameter sets of a modified version of boughton s 1984 sfb model using data from two australian catchments that have low and high runoff yields for the reduced well identified parameter set the algorithms have a similar efficiency for the low yielding catchment but sce ua is almost twice as robust although the robustness of the algorithms is similar for the high yielding catchment sce ua is six times more efficient than sa sx when fitting the full parameter set the performance of sa sx deteriorated markedly for both catchments these results indicated that sce ua s use of multiple complexes and shuffling provided a more effective search of the parameter space than sa sx s single simplex with stochastic step acceptance criterion especially when the level of parameterization is increased examination of the response surface for the low yielding catchment revealed some reasons why sce ua outperformed sa sx and why probabilistic optimization algorithms can experience difficulty in locating the global optimum

generalized simulated annealing for function optimization a generalized simulated annealing method has been developed and applied to the optimization of functions possibly constrained having many local extrema the method is illustrated in some difftcult pedagogical examples and used to solve a problem analyzed by bates technometrics 25 pp 373 376 1983 for which we identify an improved optimum the sensitivity of the solution to changes in the constraints and in other specifications of the problem is analyzed and discussed

accurate computing of higher vibration modes of thin flexible structures motivated by the need for high fidelity modeling and accurate control of future hypersonic vehicles subjected to complex aerothermomechanical loads and many other such applications a novel scheme is proposed to accurately compute higher modes of vibration for one dimensional structures by coupling the classic ritz method as a predictor and the linear two point boundary value problem solver supore as a corrector the ritz method is used to compute a preliminary estimate of the natural frequencies for the desired modes these estimates are then used as initial guesses by supore which employs superposition and reorthonormalization to accurately solve the governing boundary value problem compared to a high fidelity ritz approximation or a highly refined finite element modeling procedure this is a simple computationally less expensive and yet highly accurate method to compute mode shapes for applications that require higher modes this scheme is used to compute mode shapes within an absolute and relative

a note on the use of a fuzzy approach in adaptive partitioning algorithms for global optimization in global optimization adaptive partitioning algorithms apa operate on the basis of partitioning the feasible region into subregions sampling and evaluating each subregion and selecting one or more subregions for repartitioning the purpose of the repartitioning process is to locate a narrow neighborhood around the global optimum in this correspondence we propose to use a fuzzy approach in the assessment of subregions using random samples taken from these subregions we discuss different types of uncertainties involved in apa and we conclude that the use of a fuzzy approach in the assessment of subregions is in concurrence with apa s convergence property we provide numerical results for the fuzzy approach on 13 test functions from the literature

fluctuation effects in coagulation processes abstract we study diffusion controlled coagulation processes in the system with a finite density of droplets using the fock space formalism we find that the effective reaction rate has a correction to smoluchowski s classical rate constant for three dimensional systems we also predict the scaling form of the scattering function and find that our results qualitatively agree with experimental results

nature of stationarity of the natural frequencies at the natural modes in the rayleigh ritz method abstract a variational formulation of the rayleigh ritz method to obtain approximate natural frequencies and natural modes is presented the stationarity of the natural frequencies with respect to the arbitrary coefficients in the linear combination of the assumed deflection shapes and also at the natural modes is investigated it is concluded that the natural frequencies are stationary and need not always be minimum with respect to the arbitrary coefficients however they are minimum with respect to the natural modes this may provide a means of checking the accuracy of the computed natural frequencies obtained by using energy techniques such as the rayleigh ritz galerkin and finite element methods

the discrete coagulation fragmentation equations existence uniqueness and density conservation the discrete coagulation fragmentation equation describes the kinetics of cluster growth in which clusters can coagulate via binary interactions to form larger clusters or fragment to form smaller ones these models have many applications in pure and applied science ranging from cluster formation in galaxies to the kinetics of phase transformations in binary alloys our results relate to existence uniqueness density conservation and continuous dependence and they generalise the corresponding results in ref 2 for the becker doring equations for which the processes are restricted to clusters gaining or shedding one particle examples are given which illustrate the role of the assumptions on the kinetic coefficients and show the rich set of analytic phenomena supported by the general discrete coagulation fragmentation equations

free vibrations of beams with general boundary conditions a simple and unified approach is presented for the vibration analysis of a generally supported beam the flexural displacement of the beam is sought as the linear combination of a fourier series and an auxiliary polynomial function the polynomial function is introduced to take all the relevant discontinuities with the original displacement and its derivatives at the boundaries and the fourier series now simply represents a residual or conditioned displacement that has at least three continuous derivatives as a result not only is it always possible to expand the displacement in a fourier series for beams with any boundary conditions but also the solution converges at a much faster speed the reliability and robustness of the proposed technique are demonstrated through numerical examples

smoluchowski and beyond a field theoretical study of coagulation fragmentation processes adopting simple field theoretical techniques the author demonstrates how the smoluchowski approach to reaction diffusion models can be systematically corrected focusing on coagulation fragmentation processes he identifies the upper critical dimension dc 2 below which finite mobility corrections are important and full universality lattice independence etc can be achieved trends seem by direct simulation of simple lattice models for d 2 are confirmed whilst problems of interpretation exist for d 2

a hybrid genetic algorithm with the baldwin effect here we present a new hybrid genetic algorithm hga with the baldwin effect in the hga a local search is employed to change the fitness of individuals but the acquired improvements do not change the individual itself this local search step exploits the baldwin effect some numerical applications show that this algorithm can yield the global optimum more efficiently than commonly used hgas a theorem is presented that guarantees the convergence in probability of the new hga

integrated crossover rules in real coded genetic algorithms abstract modifications in crossover rules and localization of searches are suggested to the real coded genetic algorithms for continuous global optimization central to our modifications is the integration of different crossover rules within the genetic algorithm numerical experiments using a set of 50 test problems indicate that the resulting algorithms are considerably better than the previous version considered and offer a reasonable alternative to many currently available global optimization algorithms especially for problems requiring direct search type methods

search space boundary extension method in real coded genetic algorithms abstract in real coded genetic algorithms gas some crossover operators do not work well on functions which have their optimum at the corner of the search space to cope with this problem we have proposed a boundary extension method which allows individuals to be located within a limited space beyond the boundary of the search space in this paper we give an analysis of the boundary extension methods from the viewpoint of sampling bias and perform a comparative study on the effect of applying two boundary extension methods namely the boundary extension by mirroring bem and the boundary extension with extended selection bes we were able to confirm that to use sampling methods which have smaller sampling bias had good performance on both functions which have their optimum at or near the boundaries of the search space and functions which have their optimum at the center of the search space the bes sd a bes by shortest distance selection with aging had good performance on functions which have their optimum at or near the boundaries of the search space we also confirmed that applying the bes sd a did not cause any performance degradation on functions which have their optimum at the center of the search space

the role of different genetic operators in the optimization of magnetic models abstract in the paper we continue the analysis of the influence of genetic operators on the efficiency of global optimization of ising model for such a model the physical problem of finding the minimum energy is defined its solution enables to determine the phase state of system in the given temperature and consequently the phase transition point the process of minimum energy search is np hard we showed that for a selected set of crossover operators we can obtain similar behavior but different exact numerical characteristics here we study how the other procedures and parameters can influence the optimization process we take into account such features like selection mutation objective function or niching we try also to build a hybrid algorithm with a specific problem related local search procedure we hope that the presented results can indicate the great possibilities of magnetic models as a test problem

vibration of orthotropic plates discussion on the completeness of the solutions used in direct methods fil rosales marta beatriz consejo nacional de investigaciones cientificas y tecnicas centro cientifico tecnologico conicet bahia blanca argentina universidad nacional del sur departamento de ingenieria argentina