discrete riemann surfaces and the ising model we define a new theory of discrete riemann surfaces and present its basic results the key idea is to consider not only a cellular decomposition of a surface but the union with its dual discrete holomorphy is defined by a straightforward discretisation of the cauchy riemann equation a lot of classical results in riemann theory have a discrete counterpart hodge star harmonicity hodge theorem weyl s lemma cauchy integral formula existence of holomorphic forms with prescribed holonomies giving a geometrical meaning to the construction on a riemann surface we define a notion of criticality on which we prove a continuous limit theorem we investigate its connection with criticality in the ising model we set up a dirac equation on a discrete universal spin structure and we prove that the existence of a dirac spinor is equivalent to criticality

adaptive configuration of evolutionary algorithms for constrained optimization in the literature many different evolutionary algorithms eas with different search operators have been reported for solving optimization problems however no single algorithm is consistently able to solve all types of problems to overcome this problem the recent trend is to use a mix of operators within a single algorithm there are also cases where multiple methodologies each with a single search operator have been used under one approach these approaches outperformed the single operator based single algorithm approaches in this paper we propose a new algorithm framework that uses multiple methodologies where each methodology uses multiple search operators we introduce it as the ea with adaptive configuration where the first level is to decide the methodologies and the second level is to decide the search operators in this approach all operators and population sizes are updated adaptively although the framework may sound complex one can gain significant benefits from it in solving optimization problems the proposed framework has been tested by solving two sets of specialized benchmark problems the results showed a competitive if not better performance when it was compared to the state of the art algorithms moreover the proposed algorithm significantly reduces the computational time in comparison to both single and multi operator based algorithms

benchmarking optimization software with performance profiles we propose performance profiles distribution functions for a performance metric as a tool for benchmarking and comparing optimization software we show that performance profiles combine the best features of other tools for performance evaluation

an efficient hybrid multi objective particle swarm optimization with a multi objective dichotomy line search recently more research works are focused on multi objective particle swarm optimization algorithm mopso due to its ability of global and local search for solving multi objective optimization problems moops however most of existing mopsos cannot achieve satisfactory results in solution quality this paper proposes an efficient hybrid multi objective particle swarm optimization with a multi objective dichotomy line search mols named mols mopso to deal with such problem mols mopso combines an effective particle updating strategy with the local search of mols the effective particle updating strategy is used for global search to deal with premature convergence and diversity maintenance within the swarm the mols is periodically activated for fast local search to converge toward the pareto front the exploratory capabilities are enhanced more efficiently by keeping a desirable balance between global search and local search so as to ensure sufficient diversity and well distribution amongst the solutions of the non dominated fronts while retaining at the same time the convergence to the pareto optimal front comparing mols mopso with various state of the art multi objective optimization algorithms developed recently the comparative study shows the effectiveness of mols mopso which not only assures a better convergence to the pareto frontier but also illustrates a good diversity and distribution of solutions

effective lengths of intervals to improve forecasting in fuzzy time series abstract length of intervals affects forecasting results in fuzzy time series unfortunately the issue of how to determine effective lengths of intervals has not been touched in previous studies this study proposes distribution and average based length to approach this issue distribution based length is the largest length smaller than at least half the first differences of data average based length is set to one half the average of the first differences of data empirical analyses show that distribution and average based lengths are simple to calculate and can greatly improve forecasting results in particular they are superior to the randomly chosen lengths used in previous studies

a hybrid simplex search and particle swarm optimization for unconstrained optimization this paper proposes the hybrid nm pso algorithm based on the nelder mead nm simplex search method and particle swarm optimization pso for unconstrained optimization nm pso is very easy to implement in practice since it does not require gradient computation the modification of both the nelder mead simplex search method and particle swarm optimization intends to produce faster and more accurate convergence the main purpose of the paper is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy in a suite of 20 test function problems taken from the literature computational results via a comprehensive experimental study preceded by the investigation of parameter selection show that the hybrid nm pso approach outperforms other three relevant search techniques i e the original nm simplex search method the original pso and the guaranteed convergence particle swarm optimization gcpso in terms of solution quality and convergence rate in a later part of the comparative experiment the nm pso algorithm is compared to various most up to date cooperative pso cpso procedures appearing in the literature the comparison report still largely favors the nm pso algorithm in the performance of accuracy robustness and function evaluation as evidenced by the overall assessment based on two kinds of computational experience the new algorithm has demonstrated to be extremely effective and efficient at locating best practice optimal solutions for unconstrained optimization

conformal invariance of domino tiling let u be a multiply connected region in r 2 with smooth boundary let p be a polyomino in z 2 approximating u as 0 we show that for certain boundary conditions on p the height distribution on a random domino tiling dimer covering of p is conformally invariant in the limit as tends to 0 in the sense that the distribution of heights of boundary components or rather the difference of the heights from their mean values only depends on the conformal type of u the mean height is not strictly conformally invariant but transforms analytically under conformal mappings in a simple way the mean height and all the moments are explicitly evaluated

an adaptive parameter tuning of particle swarm optimization algorithm an adaptive parameter tuning of particle swarm optimization based on velocity information apso vi algorithm is proposed in this paper the velocity convergence of particles is first analyzed and the relationship between the velocity of particle and the search failures is pointed out which reveals the reasons why pso has relative poor global searching ability then this algorithm introduces the velocity information which is defined as the average absolute value of velocity of all the particles a new strategy is presented that the inertia weight is dynamically adjusted according to average absolute value of velocity which follows a given nonlinear ideal velocity by feedback control which can avoid the velocity closed to zero at the early stage under the guide of the nonlinear ideal velocity apso vi can maintain appropriate swarm diversity and alleviate the premature convergence validly numerical experiments are conducted to compare the proposed algorithm with different variants of pso on some benchmark functions experimental results show that the proposed algorithm remarkably improves the ability of pso to jump out of the local optima and significantly enhance the convergence speed and precision

the dirichlet problem for orthodiagonal maps abstract we prove that the discrete harmonic function corresponding to smooth dirichlet boundary conditions on orthodiagonal maps that is plane graphs having quadrilateral faces with orthogonal diagonals converges to its continuous counterpart as the mesh size goes to 0 this provides a convergence statement for discrete holomorphic functions similar to the one obtained by chelkak and smirnov 4 for isoradial graphs we observe that by the double circle packing theorem 3 any finite simple 3 connected planar map admits an orthodiagonal representation our result improves the work of skopenkov 41 and werness 47 by dropping all regularity assumptions required in their work and providing effective bounds in particular no bound on the vertex degrees is required thus the result can be applied to models of random planar maps that with high probability admit orthodiagonal representation with mesh size tending to 0 in a companion paper 21 we show that this can be done for the discrete mating of trees random map model of duplantier gwynne miller and sheffield 15 25

a piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization this paper presents a novel robust hybrid particle swarm optimization rhpso based on piecewise linear chaotic map pwlcm and sequential quadratic programming sqp the aim of the present research is to develop a new single objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems this novel algorithm makes the best of ergodicity of pwlcm to help pso with the global search while employing the sqp to accelerate the local search five unconstrained benchmarks eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach the simulation results compared with other state of art methods demonstrate the effectiveness and robustness of the proposed rhpso for both unconstrained and constrained problems of different dimensions

an efficient constraint handling method for genetic algorithms many real world search and optimization problems involve inequality and or equality constraints and are thus posed as constrained optimization problems in trying to solve constrained optimization problems using genetic algorithms gas or classical optimization methods penalty function methods have been the most popular approach because of their simplicity and ease of implementation however since the penalty function approach is generic and applicable to any type of constraint linear or nonlinear their performance is not always satisfactory thus researchers have developed sophisticated penalty functions specific to the problem at hand and the search algorithm used for optimization however the most difficult aspect of the penalty function approach is to find appropriate penalty parameters needed to guide the search towards the constrained optimum in this paper ga s population based approach and ability to make pair wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter careful comparisons among feasible and infeasible solutions are made so as to provide a search direction towards the feasible region once sufficient feasible solutions are found a niching method along with a controlled mutation operator is used to maintain diversity among feasible solutions this allows a real parameter ga s crossover operator to continuously find better feasible solutions gradually leading the search near the true optimum solution gas with this constraint handling approach have been tested on nine problems commonly used in the literature including an engineering design problem in all cases the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier

pareto based grouping discrete harmony search algorithm for multi objective flexible job shop scheduling abstract this paper proposes a pareto based grouping discrete harmony search algorithm pgdhs to solve the multi objective flexible job shop scheduling problem fjsp two objectives namely the maximum completion time makespan and the mean of earliness and tardiness are considered simultaneously firstly two novel heuristics and several existing heuristics are employed to initialize the harmony memory secondly multiple harmony generation strategies are proposed to improve the performance of harmony search algorithm the operation sequence in a new harmony is produced based on the encoding method and the characteristics of fjsp thirdly two local search methods based on critical path and due date are embedded to enhance the exploitation capability finally extensive computational experiments are carried out using well known benchmark instances three widely used performance measures number of non dominated solutions diversification metric and quality metric are employed to test the performance of pgdhs algorithm computational results and comparisons show the efficiency and effectiveness of the proposed pgdhs algorithm for solving multi objective flexible job shop scheduling problem

the boundary value problem for discrete analytic functions abstract this paper is on further development of discrete complex analysis introduced by r isaacs j ferrand r duffin and c mercat we consider a graph lying in the complex plane and having quadrilateral faces a function on the vertices is called discrete analytic if for each face the difference quotients along the two diagonals are equal we prove that the dirichlet boundary value problem for the real part of a discrete analytic function has a unique solution in the case when each face has orthogonal diagonals we prove that this solution uniformly converges to a harmonic function in the scaling limit this solves a problem of s smirnov from 2010 this was proved earlier by r courant k friedrichs h lewy and l lusternik for square lattices by d chelkak s smirnov and implicitly by p g ciarlet p a raviart for rhombic lattices in particular our result implies uniform convergence of the finite element method on delaunay triangulations this solves a problem of a bobenko from 2011 the methodology is based on energy estimates inspired by alternating current network theory

improved novel global harmony search with a new relaxation method for reliability optimization problems reliability engineers often try to achieve a high reliability level of electronic systems reliability optimization problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint e g the total cost weight the reliability optimization has been an active area of research for the past decades in this paper an improved novel global harmony search inghs approach is proposed to solve reliability optimization problem the inghs employs a stochastic position updating strategy to replace the position updating of nghs and thus the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms performance moreover a new constraint relaxation method also is integrated into the proposed approach applying the proposed approach to several well known benchmark problems results indicate that the inghs algorithm can find better solution effectively and efficiently furthermore the inghs algorithm is used to solve reliability optimization problems numerical results demonstrated that the inghs algorithm performs better than several state of the art algorithms in most cases

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

conformal invariance in random cluster models i holmorphic fermions in the ising model we construct discrete holomorphic observables in the ising model at criticality and show that they have conformally covariant scaling limits as mesh of the lattice tends to zero in the sequel those observables are used to construct conformally invariant scaling limits of interfaces though ising model is often cited as a classical example of conformal invariance it seems that ours is the first paper where it is actually established

improving particle swarm optimization using multi layer searching strategy abstract in recent years particle swarm optimization pso algorithm has been used to solve global optimization problems this algorithm is widely used as an effective optimization tool in various applications however traditional pso consists of only two searching layers and thus often results in premature convergence into the local minima thus multi layer particle swarm optimization mlpso is proposed in this paper to improve the performance of traditional pso by increasing the two layers of swarms to multiple layers the mlpso strategy increases the diversity of searching swarms to improve its performance when solving complex problems the experiment indicates that the novel approach improves the final results and the convergence speed

multi variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques in this paper we present a new method for multi variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques first the proposed method constructs training samples based on the variation rates of the training data set and then uses the training samples to construct fuzzy rules by making use of the fuzzy c means clustering algorithm where each fuzzy rule corresponds to a given cluster then we determine the weight of each fuzzy rule with respect to the input observations and use such weights to determine the predicted output based on the multiple fuzzy rules interpolation scheme we apply the proposed method to the temperature prediction problem and the taiwan stock exchange capitalization weighted stock index taiex data the experimental results show that the proposed method produces better forecasting results than several existing methods

a projection method for convex constrained monotone nonlinear equations with applications in this paper we present a projection method to solve monotone nonlinear equations with convex constraints this method can be viewed as an extension of cg descent method which is one of the most effective conjugate gradient methods for solving unconstrained optimization problems because of derivative free and low storage the proposed method can be used to solve large scale nonsmooth monotone nonlinear equations its global convergence is established under some appropriate conditions preliminary numerical results show that the proposed method is effective and promising moreover we also successfully use the proposed method to solve the sparse signal reconstruction in compressive sensing

all dimension neighborhood based particle swarm optimization with randomly selected neighbors particle swarm optimization pso is widely used for solving various optimization problems since it has few parameters and is easy to implement however canonical pso generally suffers from premature convergence because it usually loses diversity too rapidly during the evolutionary process to improve the performance of pso on complex problems an all dimension neighborhood based pso with randomly selected neighbors learning strategy adn rsn pso is proposed in this study the randomly selected neighbors rsn learning strategy is adopted in the early stage of pso to enhance the swarm diversity while the all dimension neighborhood adn strategy is utilized in the later stage to accelerate the convergence rate the adn strategy enhances the local search capability around the global best solution in a dimension by dimension manner and the search distance is adapted by shrinking and random expansion operators experimental results show that adn pso can improve the exploitation capability of the global version of pso to test the performance of the proposed adn rsn pso comparison tests on the cec2013 test suite are carried out the comparison results reveal that adn rsn pso outperforms other peer pso variants in the end the proposed adn rsn pso is applied to the radar system design problem to demonstrate its potential in real life applications

an intelligent augmentation of particle swarm optimization with multiple adaptive methods over the last two decades the newly developed optimization technique particle swarm optimization pso has attracted great attention two common criticisms exist first most existing psos are designed for a specific search space thus an algorithm performing well on a diverse set of problems is lacking secondly pso suffers premature convergence to address the first issue we propose to augment pso via the fusion of multiple search methods an intelligent selection mechanism is developed based on an effectiveness index to trigger appropriate search methods in this research two search techniques are studied a non uniform mutation based method and an adaptive sub gradient method we further improve the proposed pso using adaptive cauchy mutation to prevent premature convergence as a result an augmented pso with multiple adaptive methods pso mam is proposed the performance of pso mam is tested on 43 functions uni modal multi modal non separable shifted rotated noisy and mis scaled functions the results are compared in terms of solution quality and convergence speed with 10 published pso methods the experimental results demonstrate pso mam outperforms the comparison algorithms on 36 out of 43 functions we conclude while promising there is still room for improving pso mam on complex multi modal functions e g rotated multi modal functions

handling forecasting problems using fuzzy time series abstract in 6 9 song et al proposed fuzzy time series models to deal with forecasting problems in 10 sullivan and woodall reviewed the first order time invariant fuzzy time series model and the first order time variant model proposed by song and chissom 6 8 where the models are compared with each other and with a time invariant markov model using linguistic labels with probability distributions in this paper we propose a new method to forecast university enrollments where the historical enrollments of the university of alabama shown in 7 8 are used to illustrate the forecasting process the average forecasting errors and the time complexity of these methods are compared the proposed method is more efficient than the ones presented in 7 8 10 due to the fact that the proposed method simplifies the arithmetic operation process furthermore the average forecasting error of the proposed method is smaller than the ones presented in 2 7 8

taiex forecasting based on fuzzy time series and fuzzy variation groups in this paper we present a new method to forecast the daily taiwan stock exchange capitalization weighted stock index taiex based on fuzzy time series and fuzzy variation groups where the main input factor is the previous day s taiex and the secondary factor is either the dow jones the nasdaq the m 1b or their combination first the proposed method fuzzifies the historical training data of the taiex into fuzzy sets to form fuzzy logical relationships second it groups the fuzzy logical relationships into fuzzy logical relationship groups flrgs based on the fuzzy variations of the secondary factor third it evaluates the leverage of the fuzzy variations between the main factor and the secondary factor to construct fuzzy variation groups fourth it gets the statistics of the fuzzy variations appearing in each fuzzy variation group fifth it calculates the weights of the statistics of the fuzzy variations appearing in each fuzzy variation group respectively finally based on the weights of the statistics of the fuzzy variations appearing in the fuzzy variation groups and the flrgs it performs the forecasting of the daily taiex because the proposed method uses both fuzzy variation groups and flrgs to analyze in detail the historical training data it gets higher forecasting accuracy rates to forecast the taiex than the existing methods

multi objective particle swarm optimization with time variant inertia and acceleration coefficients in this article we describe a novel particle swarm optimization pso approach to multi objective optimization moo called time variant multi objective particle swarm optimization tv mopso tv mopso is made adaptive in nature by allowing its vital parameters viz inertia weight and acceleration coefficients to change with iterations this adaptiveness helps the algorithm to explore the search space more efficiently a new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non dominated fronts while retaining at the same time the convergence to the pareto optimal front tv mopso has been compared with some recently developed multi objective pso techniques and evolutionary algorithms for 11 function optimization problems using different performance measures

hybrid harmony search particle swarm optimization with global dimension selection this study presents a hybrid harmony search particle swarm optimization with global dimension selection hhspso gds for improving the performance of particle swarm optimization pso in hhspso gds a new global velocity updating strategy is introduced to enhance the neighborhood region search of the current best solution and to get a better trade off between convergence rate and robustness additionally a dynamic non linear decreased inertia weight is utilized to balance the global exploration and local exploitation moreover the best worst improvisation mechanism of harmony search hs is implanted in the hhspso gds algorithm and a global dimension selection is employed in the improvisation process which can effectively accelerate convergence global best information sharing strategy is developed to link the two layer exploration frames pso and hs finally a comprehensive experimental study is conducted on a large number of benchmark functions the experimental results reveal that hhspso gds performs better in terms of the quality of solution convergence rate robustness and scalability compared to various state of the art psos and other meta heuristic search algorithms

an improved harmony search algorithm for solving optimization problems this paper develops an improved harmony search ihs algorithm for solving optimization problems ihs employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search hs algorithm in this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented the ihs algorithm has been successfully applied to various benchmarking and standard engineering optimization problems numerical results reveal that the proposed algorithm can find better solutions when compared to hs and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems

nelder mead simplex modifications for simulation optimization when the nelder mead method is used to optimize the expected response of a stochastic system e g an output of a discrete event simulation model the simplex resizing steps of the method introduce risks of inappropriate termination we give analytical and empirical results describing the performance of nelder mead when it is applied to a response function that incorporates an additive white noise error and we use these results to develop new modifications of nelder mead that yield improved estimates of the optimal expected response compared to nelder mead the best performance was obtained by a modified method rs s9 in which a the best point in the simplex is reevaluated at each shrink step and b the simplex is reduced by 10 rather than 50 at each shrink step in a suite of 18 test problems that were adapted from the minpack collection of netlib the expected response at the estimated optimal point obtained by rs s9 had errors that averaged 15 less than at the original method s estimated optimal point at an average cost of three times as many function evaluations two well known existing modifications for stochastic responses the n 3 rule and the next to worst rule were found to be inferior to the new modification rs s9

a new inexact alternating directions method for monotone variational inequalities the alternating directions method adm is an effective method for solving a class of variational inequalities vi when the proximal and penalty parameters in sub vi problems are properly selected in this paper we propose a new adm method which needs to solve two strongly monotone sub vi problems in each iteration approximately and allows the parameters to vary from iteration to iteration the convergence of the proposed adm method is proved under quite mild assumptions and flexible parameter conditions

an artificial bee colony algorithm for the maximally diverse grouping problem in this paper an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem this complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements the artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees the behaviour of this algorithm is determined by two search strategies an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions more specifically the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm in addition it incorporates an improvement procedure to enhance the intensification capability through an analysis of the experimental results the highly effective performance of the proposed algorithm is shown in comparison to the current state of the art algorithms which address the problem

a type 2 fuzzy time series model for stock index forecasting abstract most conventional fuzzy time series models type 1 models utilize only one variable in forecasting furthermore only part of the observations in relation to that variable are used to utilize more of that variable s observations in forecasting this study proposes the use of a type 2 fuzzy time series model in such a type 2 model extra observations are used to enrich or to refine the fuzzy relationships obtained from type 1 models and then to improve forecasting performance the taiwan stock index the taiex is used as the forecasting target the study period extends over the 2000 2003 period the taiex from january to october in each year is used for the estimation while that covering november and december is used for forecasting the empirical analyses show that type 2 model outperforms type 1 model

weighted fuzzy time series models for taiex forecasting abstract this study proposes weighted models to tackle two issues in fuzzy time series forecasting namely recurrence and weighting it is argued that recurrent fuzzy relationships which were simply ignored in previous studies should be considered in forecasting it is also recommended that different weights be assigned to various fuzzy relationships in previous studies these fuzzy relationships were treated as if they were equally important which might not have properly reflected the importance of each individual fuzzy relationship in forecasting the weighted models are compared with the local regression models in which weight functions also play an important role both models are different by nature but certain theoretical backgrounds in local regression models are adopted by using the taiwan stock index as the forecasting target the empirical results show that the weighted model outperforms one of the conventional fuzzy time series models

particle swarm optimization hybridization perspectives and experimental illustrations metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods in the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic particle swarm optimization or pso hybridization is a method of combining two or more techniques in a judicious manner such that the resulting algorithm contains the positive features of both or all the algorithms depending on the algorithm s used we made three classifications as i hybridization of pso and genetic algorithms ii hybridization of pso with differential evolution and iii hybridization of pso with other techniques where other techniques include various local and global search methods besides giving the review we also show a comparison of three hybrid pso algorithms hybrid differential evolution particle swarm optimization de pso adaptive mutation particle swarm optimization ampso and hybrid genetic algorithm particle swarm optimization ga pso on a test suite of nine conventional benchmark problems

integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems heuristic optimization provides a robust and efficient approach for solving complex real world problems the aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques particle swarm optimization pso and genetic algorithms ga our approach integrates the merits of both ga and pso and it has two characteristic features firstly the algorithm is initialized by a set of random particles which travel through the search space during this travel an evolution of these particles is performed by integrating pso and ga secondly to restrict velocity of the particles and control it we introduce a modified constriction factor finally the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution

fuzzy global optimization of complex system reliability the problem of optimizing the reliability of complex systems has been modeled as a fuzzy multi objective optimization problem three different kinds of optimization problems reliability optimization of a complex system with constraints on cost and weight optimal redundancy allocation in a multistage mixed system with constraints on cost and weight and optimal reliability allocation in a multistage mixed system with constraints on cost weight and volume have been solved four numerical examples have been solved to demonstrate the effectiveness of the present methodology the influence of various kinds of aggregators is also studied the inefficiency of the noncompensatory min operator has been demonstrated one of the well known global optimization meta heuristics the threshold accepting has been invoked to take care of the optimization part of the model a software has been developed to implement the above model the results obtained are encouraging

a comparison of fuzzy forecasting and markov modeling abstract fuzzy time series models were introduced by song and chissom fuzzy sets and systems 54 1993 269 277 54 1993 1 9 62 1994 1 8 to model and forecast processes whose values are described by linguistic variables song and chissom used as an application the forecasting of educational enrollments this paper reviews the two methods set forth by song and chissom a first order time invariant fuzzy time series model and a first order time variant model these models are compared with each other and with a time invariant markov model using linguistic labels with probability distributions the results of these methods for the enrollment data are compared with three traditional time series models a first order autoregressive ar 1 model and two second order auto regressive ar 2 models all of which are time invariant

memetic multiobjective particle swarm optimization based radial basis function network for classification problems this paper presents a new multiobjective evolutionary algorithm applied to a radial basis function rbf network design based on multiobjective particle swarm optimization augmented with local search features the algorithm is named the memetic multiobjective particle swarm optimization rbf network mpson because it integrates the accuracy and structure of an rbf network the proposed algorithm is implemented on two class and multiclass pattern classification problems with one complex real problem the experimental results indicate that the proposed algorithm is viable and provides an effective means to design multiobjective rbf networks with good generalization capability and compact network structure the accuracy and complexity of the network obtained by the proposed algorithm are compared with the memetic non dominated sorting genetic algorithm based rbf network mgan through statistical tests this study shows that mpson generates rbf networks coming with an appropriate balance between accuracy and simplicity outperforming the other algorithms considered

the differential ant stigmergy algorithm ant colony optimization aco is a popular swarm intelligence scheme known for its efficiency in solving combinatorial optimization problems however despite some extensions of this approach to continuous optimization high dimensional problems remain a challenge for aco this paper presents an aco based algorithm for numerical optimization capable of solving high dimensional real parameter optimization problems the algorithm called the differential ant stigmergy algorithm dasa transforms a real parameter optimization problem into a graph search problem the parameters differences assigned to the graph vertices are used to navigate through the search space we compare the algorithm results with the results of previous studies on recent benchmark functions and show that the dasa is a competitive continuous optimization algorithm that solves high dimensional problems effectively and efficiently

an improved global best harmony search algorithm this paper introduces an improved global best harmony search ighs algorithm the proposed modifications effectively combines a novel improvisation scheme with a previously developed mechanism for updating the pitch adjustment rate par and the distance bandwidth bw the aim of this modification is to efficiently investigate the search space by going through the stages of exploration and exploitation the proposed algorithm is compared against seven previous modifications to hs using rigorous statistical tests when applied to the cec05 benchmark functions showing a superior performance on most of the tested functions

a conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing abstract cg descent is a state of the art algorithm to solve large scale unconstrained minimization problems however research activities on cg descent in some other scenarios are relatively fewer in this paper by combining with the projection method of solodov and svaiter we extend cg descent to solve large scale nonlinear convex constrained monotone equations the proposed method does not require the jacobian information even though it does not store any matrix at each iteration it thus has the potential to solve large scale non smooth problems under some mild conditions we show that the proposed method converges globally primary numerical results illustrate that the proposed method works quite well moreover we also extend this method to solve the l 1 norm regularized problems to decode a sparse signal in compressive sensing performance comparisons show that the proposed method is practical efficient and competitive with the compared ones

low complexity metaheuristics for joint ml estimation problems abstract joint maximum likelihood ml estimation of multiple parameters is an important problem with wide spread relevance in many domains the high computational complexity involved in joint ml problems has led to the search for more efficient methods efficient heuristic algorithms for joint ml problems can be developed by exploiting the characteristics of the objective functions used in the estimation problem this paper proposes a novel reformulation of existing heuristic algorithms which considerably reduces their computational complexity with significant improvement in performance the method is applicable for joint maximum likelihood estimation problems with cost functions that exhibit asymptotic separability with increase in observation vector size the proposed method is adopted to five recently discovered heuristic algorithms and consequently applied to a relevant recent signal processing problem in wireless communication it is found that the reformulated algorithms deliver both reduced computational complexity as well as better mean square error mse performance the significant features of the proposed method are substantiated through extensive computer simulation studies

global best harmony search harmony search hs is a new meta heuristic optimization method imitating the music improvisation process where musicians improvise their instruments pitches searching for a perfect state of harmony a new variant of hs called global best harmony search ghs is proposed in this paper where concepts from swarm intelligence are borrowed to enhance the performance of hs the performance of the ghs is investigated and compared with hs and a recently developed variation of hs the experiments conducted show that the ghs generally outperformed the other approaches when applied to ten benchmark problems the effect of noise on the performance of the three hs variants is investigated and a scalability study is conducted the effect of the ghs parameters is analyzed finally the three hs variants are compared on several integer programming test problems the results show that the three approaches seem to be an efficient alternative for solving integer programming problems 2007 elsevier inc all rights reserved

a self adaptive global best harmony search algorithm for continuous optimization problems this paper presents a self adaptive global best harmony search sghs algorithm for solving continuous optimization problems in the proposed sghs algorithm a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies the harmony memory consideration rate hmcr and pitch adjustment rate par are dynamically adapted by the learning mechanisms proposed the distance bandwidth bw is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature the computational results show that the proposed sghs algorithm is more effective in finding better solutions than the state of the art harmony search hs variants

spectral residual method without gradient information for solving large scale nonlinear systems of equations a fully derivative free spectral residual method for solving largescale nonlinear systems of equations is presented it uses in a systematic way the residual vector as a search direction a spectral steplength that produces a nonmonotone process and a globalization strategy that allows for this nonmonotone behavior the global convergence analysis of the combined scheme is presented an extensive set of numerical experiments that indicate that the new combination is competitive and frequently better than well known newton krylov methods for largescale problems is also presented

adaptive fuzzy particle swarm optimization for global optimization of multimodal functions this paper proposes an adaptive fuzzy pso afpso algorithm based on the standard particle swarm optimization spso algorithm the proposed afpso utilizes fuzzy set theory to adjust pso acceleration coefficients adaptively and is thereby able to improve the accuracy and efficiency of searches incorporating this algorithm with quadratic interpolation and crossover operator further enhances the global searching capability to form a new variant called afpso qi we compared the proposed afpso and its variant afpso qi with spso quadratic interpolation pso qipso unified pso upso fully informed particle swarm fips dynamic multi swarm pso dmspso and comprehensive learning pso clpso across sixteen benchmark functions the proposed algorithms performed well when applied to minimization problems for most of the multimodal functions considered

on convergence of the multi objective particle swarm optimizers several variants of the particle swarm optimization pso algorithm have been proposed in recent past to tackle the multi objective optimization mo problems based on the concept of pareto optimality although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of pso as a single objective optimizer till date to the best of our knowledge no such analysis exists for the multi objective pso mopso algorithms this paper presents a first simple analysis of the general pareto based mopso and finds conditions on its most important control parameters the inertia factor and acceleration coefficients that govern the convergence behavior of the algorithm to the optimal pareto front in the objective function space computer simulations over benchmark mo problems have also been provided to substantiate the theoretical derivations

an improved particle swarm optimization algorithm an improved particle swarm optimization ipso is proposed in this paper in the new algorithm a population of points sampled randomly from the feasible space then the population is partitioned into several sub swarms each of which is made to evolve based on particle swarm optimization pso algorithm at periodic stages in the evolution the entire population is shuffled and then points are reassigned to sub swarms to ensure information sharing this method greatly elevates the ability of exploration and exploitation simulations for three benchmark test functions show that ipso possesses better ability to find the global optimum than that of the standard pso algorithm compared with pso ipso is also applied to identify the hydrologic model the results show that ipso remarkably improves the calculation accuracy and is an effective global optimization to calibrate hydrologic model

an interior point method for semidefinite programming we propose a new interior point based method to minimize a linear function of a matrix variable subject to linear equality and inequality constraints over the set of positive semidefinite matrices

rhombic embeddings of planar quad graphs given a finite or infinite planar graph all of whose faces have degree 4 we study embeddings in the plane in which all edges have length 1 that is in which every face is a rhombus we give a necessary and sucient condition for the existence of such an embedding as well as a description of the set of all such embeddings r etant donne un graphe planaire fini ou infini dont toutes les faces sont de degre 4 onetudie ses plongements dans le plan dont toutes les aretes sont de longueur 1 c estdire dont toutes les faces sont des losanges on donne une condition necessaire et susante pour l existence d un tel plongement et on decrit l ensemble de ces plongements

a study of particle swarm optimization particle trajectories particle swarm optimization pso has shown to be an efficient robust and simple optimization algorithm most of the pso studies are empirical with only a few theoretical analyses that concentrate on understanding particle trajectories these theoretical studies concentrate mainly on simplified pso systems this paper overviews current theoretical studies and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term the paper also provides a formal proof that each particle converges to a stable point an empirical analysis of multi dimensional stochastic particles is also presented experimental results are provided to support the conclusions drawn from the theoretical findings

global dynamic harmony search algorithm gdhs this paper presents a new modification of harmony search hs algorithm to improve its accuracy and convergence speed and eliminates setting parameters that have to be defined before optimization process and it is difficult to predict fixed values for all kinds of problems the proposed algorithm is named global dynamic harmony search gdhs in this modification all the key parameters are changed to dynamic mode and there is no need to predefine any parameters also the domain is changed to dynamic mode to help a faster convergence two experiments with large sets of benchmark functions are executed to compare the proposed algorithms with other ones in the first experiment 15 benchmark problems are used to compare the proposed algorithm with other similar algorithms based on the harmony search method and in the second experiment 47 benchmark problems are used to compare the performance of the gdhs with other algorithms from different families including ga pso de and abc algorithms results showed that the proposed algorithm outperforms the other algorithms considering the point that the gdhs does not require any predefined parameter

uniform spanning forests we study uniform spanning forest measures on infinite graphs which are weak limits of uniform spanning tree measures from finite subgraphs these limits can be taken with free fsf or wired wsf boundary conditions pemantle proved that the free and wired spanning forests coincide in z d and that they give a single tree iff d 4 in the present work we extend pemantle s alternative to general graphs and exhibit further connections of uniform spanning forests to random walks potential theory invariant percolation and amenability the uniform spanning forest model is related to random cluster models in statistical physics but because of the preceding connections its analysis can be carried further among our results are the following the fsf and wsf in a graph g coincide iff all harmonic dirichlet functions on g are constant the tail fields of the wsf and the fsf are trivial on any graph on any cayley graph that is not a finite extension of z all component trees of the wsf have one end this is new in z d for d 5 on any tree as well as on any graph with spectral radius less than 1 a s all components of the wsf are recurrent the basic topology of the free and the wired uniform spanning forest measures on lattices in hyperbolic space h d is analyzed a cayley graph is amenable iff for all e 0 the union of the wsf and bernoulli percolation with parameter e is connected harmonic measure from infinity is shown to exist on any recurrent proper planar graph with finite codegrees we also present numerous open problems and conjectures

greedy discrete particle swarm optimization for large scale social network clustering social computing is a new paradigm for information and communication technology social network analysis is one of the theoretical underpinnings of social computing community structure detection is believed to be an effective tool for social network analysis uncovering community structures in social networks can be regarded as clustering optimization problems because social networks are characterized by dynamics and huge volumes of data conventional nature inspired optimization algorithms encounter serious challenges when applied to solving large scale social network clustering optimization problems in this study we put forward a novel particle swarm optimization algorithm to reveal community structures in social networks the particle statuses are redefined under a discrete scenario the status updating rules are reconsidered based on the network topology a greedy strategy is designed to drive particles to a promising region to this end a greedy discrete particle swarm optimization framework for large scale social network clustering is suggested to determine the performance of the algorithm extensive experiments on both synthetic and real world social networks are carried out we also compare the proposed algorithm with several state of the art network community clustering methods the experimental results demonstrate that the proposed method is effective and promising for social network clustering

solving bi level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm bi level linear programming consisting of upper level and lower level objectives is a technique for modeling decentralized decision this study presents a hybrid of immune genetic algorithm and vector controlled particle swarm optimization igvpso to solve the bi level linear programming problem blpp it is applied to a supply chain model that is a blpp using four problems from the literature and the supply chain distribution models the computational results indicate that the proposed method is superior to some algorithms

an efficient implementation of merrill s method for sparse or partially separable systems of nonlinear equations when merrill s method without an extra dimension is applied to solving sparse or partially separable systems of nonlinear equations the computational efficiency can be further improved i e a larger piece of linearity can be traversed in one step by using a suitable linear system one of the linear systems is updated and the corresponding technique is shown to update information about the linear system and the large piece in the implementation of the method some numerical results of the method support the claim that it is efficient also some mistakes from a previous paper in which the main technique exploiting sparsity was proposed are corrected

a modified particle swarm optimizer with dynamic adaptation this paper proposes a modified particle swarm optimization algorithm with dynamic adaptation in this algorithm a modified velocity updating formula of the particle is used where the randomness in the course of updating particle velocity is relatively decreased and the inertia weight of each particle is different moreover this algorithm introduces two parameter describing the evolving state of the algorithm the evolution speed factor and aggregation degree factor by analyzing the influence of two parameters on the pso search ability a new strategy is presented that the inertia weight dynamically changes based on the run and evolution state in the strategy the inertia weight is given by a function of evolution speed factor and aggregation degree factor and the value of inertia weight is dynamically adjusted according to the evolution speed and aggregation degree the feature of the proposed algorithm is analyzed and several testing functions are performed in simulation study experimental results show that the proposed algorithm remarkably improves the ability of pso to jump out of the local optima and significantly enhance the convergence precision

memetic binary particle swarm optimization for discrete optimization problems in recent decades many researchers have been interested in algorithms inspired by the observation of natural phenomena to solve optimization problems among them meta heuristic algorithms have been extensively applied in continuous real and discrete binary search spaces such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities in this study a memetic binary particle swarm optimization bpso scheme is introduced based on hybrid local and global searches in bpso the algorithm binary hybrid topology particle swarm optimization bhtpso is used to solve the optimization problems in the binary search spaces in addition a variant of the proposed algorithm binary hybrid topology particle swarm optimization quadratic interpolation bhtpso qi is proposed to enhance the global searching capability these algorithms are tested on two set of problems in the binary search space several nonlinear high dimension functions and benchmarks for the 0 1 multidimensional knapsack problem mkp are employed to evaluate their performances their results are compared with some well known modified binary pso and binary gravitational search algorithm bgsa the experimental results showed that the proposed methods improve the performance of bpso in terms of convergence speed and solution accuracy

heuristic models of fuzzy time series for forecasting abstract song and chissom first proposed the definitions of fuzzy time series and time invariant and variant models of fuzzy time series chen then proposed arithmetic operations to replace the complex computations in song and chissom s models this study proposes heuristic models by integrating problem specific heuristic knowledge with chen s model to improve forecasting this is because chen s model was easy to calculate was straightforward to integrate heuristic knowledge and forecast better than the others both university enrollment and futures index are chosen as the forecasting targets the empirical analyses show that the heuristic models reflect the fluctuations in fuzzy time series better and provide better overall forecasting results than the previous models

a globally convergent bfgs method for nonlinear monotone equations without any merit functions r since 1965 there has been significant progress in the theoretical study on quasi newton methods for solving nonlinear equations especially in the local convergence analysis however the study on global convergence of quasi newton methods is relatively fewer especially for the bfgs method to ensure global convergence some merit function such as the squared norm merit function is typically used in this paper we propose an algorithm for solving nonlinear monotone equations which combines the bfgs method and the hyperplane projection method we also prove that the proposed bfgs method converges globally if the equation is monotone and lipschitz continuous without differentiability requirement on the equation which makes it possible to solve some nonsmooth equations an attractive property of the proposed method is that its global convergence is independent of any merit function we also report some numerical results to show efficiency of the proposed method

capso centripetal accelerated particle swarm optimization meta heuristic search algorithms are developed to solve optimization problems such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities swarm intelligence si algorithms comprise a branch of meta heuristic algorithms that imitate the behavior of insects birds fishes and other natural phenomena to find solutions for complex optimization problems in this study an improved particle swarm optimization pso scheme combined with newton s laws of motion the centripetal accelerated particle swarm optimization capso scheme is introduced capso accelerates the learning and convergence of optimization problems in addition the binary mode of the proposed algorithm binary centripetal accelerated particle swarm optimization bcapso is introduced for binary search spaces these algorithms are evaluated using nonlinear benchmark functions and the results are compared with the gravitational search algorithm gsa and pso in both the real and the binary search spaces moreover the performance of capso in solving the functions is compared with some well known pso algorithms in the literature the experimental results showed that the proposed methods enhance the performance of pso in terms of convergence speed solution accuracy and global optimality

universality in the 2d ising model and conformal invariance of fermionic observables it is widely believed that the celebrated 2d ising model at criticality has a universal and conformally invariant scaling limit which is used in deriving many of its properties however no mathematical proof has ever been given and even physics arguments support a priori weaker mobius invariance we introduce discrete holomorphic fermions for the 2d ising model at criticality on a large family of planar graphs we show that on bounded domains with appropriate boundary conditions those have universal and conformally invariant scaling limits thus proving the universality and conformal invariance conjectures

a note on teaching learning based optimization algorithm teaching learning based optimization tlbo seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances it is reported that it outperforms some of the well known metaheuristics regarding constrained benchmark functions constrained mechanical design and continuous non linear numerical optimization problems such a breakthrough has steered us towards investigating the secrets of tlbo s dominance this paper reports our findings on tlbo qualitatively and quantitatively through code reviews and experiments respectively our findings have revealed three important mistakes regarding tlbo 1 at least one unreported but important step 2 incorrect formulae on a number of fitness function evaluations and 3 misconceptions about parameter less control additionally unfair experimental settings conditions were used to conduct experimental comparisons e g different stopping criteria the experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy the ultimate goal of this paper is to provide reminders for metaheuristics researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects and to allow fair comparisons of the tlbo algorithm to be made with other metaheuristic algorithms

taiex forecasting based on fuzzy time series particle swarm optimization techniques and support vector machines in this paper a new method for forecasting the taiex is presented based on fuzzy time series particle swarm optimization techniques and support vector machines the proposed method to forecast the taiex is based on the slope of one day variation of the taiex and the slope of two days average variation of the taiex because the slope of two days average variation of the taiex is smoother than the slope of one day variation of the taiex it is chosen to define the universe of discourse the particle swarm optimization techniques are used to get optimal intervals in the universe of discourse the support vector machine is used to classify the training data set the first feature and the second feature of the support vector machine are the slope of one day variation and the slope of two days average variation of the taiex respectively the experimental results show that the proposed method outperforms the existing methods for forecasting the taiex

the laplacian and dirac operators on critical planar graphs on a periodic planar graph whose edge weights satisfy a certain simple geometric condition the discrete laplacian and dirac operators have the property that their determinants and inverses only depend on the local geometry of the graph we obtain explicit expressions for the logarithms of the normalized determinants as well as the inverses of these operators we relate the logarithm of the determinants to the volume plus mean curvature of an associated hyperbolic ideal polyhedron in the associated dimer and spanning tree models for which the determinants of the dirac operator and the laplacian respectively play the role of the partition function this allows us to compute the entropy and correlations in terms of the local geometry in addition we define a continuous family of special discrete holomorphic functions which via convolutions gives a general process for constructing discrete holomorphic functions and discrete harmonic functions on critical planar graphs

discrete complex analysis on isoradial graphs abstract we study discrete complex analysis and potential theory on a large family of planar graphs the so called isoradial ones along with discrete analogues of several classical results we prove uniform convergence of discrete harmonic measures green s functions and poisson kernels to their continuous counterparts among other applications the results can be used to establish universality of the critical ising and other lattice models

an effective immune based two phase approach for the optimal reliability redundancy allocation problem abstract highly reliable systems can reduce loss of money and time in practice system reliability can be enhanced by i increasing component reliabilities and or ii providing redundancy at the component level a trade off between these two options is necessary for nonlinear constrained reliability optimization the problem of maximizing system reliability through component reliability choices and component redundancy is called as reliability redundancy allocation problem and it is a difficult but realistic nonlinear mixed integer optimization problem in this paper under nonlinear constraints of weight cost and volume we propose a new immune based two phase approach to solve the reliability redundancy allocation problem in the first phase an immune based algorithm ia is developed to solve the allocation problem and in the second phase we present a new procedure to improve the solutions by ia numerical results of four benchmark problems are reported and compared as shown the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature

a hybrid particle swarm optimization back propagation algorithm for feedforward neural network training the particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search but around global optimum the search process will become very slow on the contrary the gradient descending method can achieve faster convergent speed around global optimum and at the same time the convergent accuracy can be higher so in this paper a hybrid algorithm combining particle swarm optimization pso algorithm with back propagation bp algorithm also referred to as pso bp algorithm is proposed to train the weights of feedforward neural network fnn the hybrid algorithm can make use of not only strong global searching ability of the psoa but also strong local searching ability of the bp algorithm in this paper a novel selection strategy of the inertial weight is introduced to the pso algorithm in the proposed pso bp algorithm we adopt a heuristic way to give a transition from particle swarm search to gradient descending search in this paper we also give three kind of encoding strategy of particles and give the different problem area in which every encoding strategy is used the experimental results show that the proposed hybrid pso bp algorithm is better than the adaptive particle swarm optimization algorithm apsoa and bp algorithm in convergent speed and convergent accuracy

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

niching particle swarm optimization with local search for multi modal optimization multimodal optimization is still one of the most challenging tasks for evolutionary computation in recent years many evolutionary multi modal optimization algorithms have been developed all these algorithms must tackle two issues in order to successfully solve a multi modal problem how to identify multiple global local optima and how to maintain the identified optima till the end of the search for most of the multi modal optimization algorithms the fine local search capabilities are not effective if the required accuracy is high these algorithms fail to find the desired optima even after converging near them to overcome this problem this paper integrates a novel local search technique with some existing pso based multimodal optimization algorithms to enhance their local search ability the algorithms are tested on 14 commonly used multi modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations

discrete particle swarm optimization for high order graph matching high order graph matching aims at establishing correspondences between two sets of feature points using high order constraints it is usually formulated as an np hard problem of maximizing an objective function this paper introduces a discrete particle swarm optimization algorithm for resolving high order graph matching problems which incorporates several re defined operations a problem specific initialization method based on heuristic information and a problem specific local search procedure the proposed algorithm is evaluated on both synthetic and real world datasets its outstanding performance is validated in comparison with three state of the art approaches

a conjugate gradient projection method for solving equations with convex constraints abstract in this paper we propose a derivative free iterative method for a class of equations with convex constraints appearing in a variety of the practical problems such as compressing sensing fluid mechanics plasma physics nonlinear optics and solid state in the iteration our search direction can be viewed as an extension of a modified three term cg method by an appropriate line search and the projection step our method is convergent to the solution our method inherits the advantages of cg method and projection method and thus is suitable for solving large scale non smooth problem under the assumption that f is lipschitz continuous and satisfies a weaker condition of monotonicity our method is globally convergent numerical results show the efficiency of the proposed method