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

teaching learning based optimization with dynamic group strategy for global optimization abstract global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence in recent years there have been some significant developments in these areas regarding the solution of global optimization problems in this paper we propose an improved teaching learning based optimization tlbo algorithm with dynamic group strategy dgs for global optimization problems different to the original tlbo algorithm dgstlbo enables each learner to learn from the mean of his corresponding group rather than the mean of the class in the teacher phase furthermore each learner employs the random learning strategy or the quantum behaved learning strategy in his corresponding group in the learner phase regrouping occurs dynamically after a certain number of generations helping to maintain the diversity of the population and discourage premature convergence to verify the feasibility and effectiveness of the proposed algorithm experiments are conducted on 18 numerical benchmark functions in 10 30 and 50 dimensions the results show that the proposed dgstlbo algorithm is an effective method for global optimization problems

exact simulation of stochastic volatility and other affine jump diffusion processes the stochastic differential equations for affine jump diffusion models do not yield exact solutions that can be directly simulated discretization methods can be used for simulating security prices under these models however discretization introduces bias into the simulation results and a large number of time steps may be needed to reduce the discretization bias to an acceptable level this paper suggests a method for the exact simulation of the stock price and variance under hestons stochastic volatility model and other affine jump diffusion processes the sample stock price and variance from the exact distribution can then be used to generate an unbiased estimator of the price of a derivative security we compare our method with the more conventional euler discretization method and demonstrate the faster convergence rate of the error in our method specifically our method achieves an o s 1 2 convergence rate where s is the total computational budget the convergence rate for the euler discretization method is o s 1 3 or slower depending on the model coefficients and option payoff function

exact sampling of jump diffusions this paper develops a method for the exact simulation of a skeleton a hitting time and other functionals of a one dimensional jump diffusion with state dependent drift volatility jump intensity and jump size the method requires the drift function to be c1 the volatility function to be c2 and the jump intensity function to be locally bounded no further structure is imposed on these functions the method leads to unbiased simulation estimators of security prices transition densities hitting probabilities and other quantities numerical results illustrate its features

an euler type method for the strong approximation of the cox ingersoll ross process we analyse the strong approximation of the cox ingersoll ross cir process in the regime where the process does not hit zero by a positivity preserving drift implicit euler type method as an error criterion we use the p th mean of the maximum distance between the cir process and its approximation on a finite time interval we show that under mild assumptions on the parameters of the cir process the proposed method attains up to a logarithmic term the convergence of order 1 2 this agrees with the standard rate of the strong convergence for global approximations of stochastic differential equations with lipschitz coefficients despite the fact that the cir process has a non lipschitz diffusion coefficient

density approximations for multivariate affine jump diffusion processes we introduce closed form transition density expansions for multivariate affine jump diffusion processes the expansions rely on a general approximation theory which we develop in weighted hilbert spaces for random variables which possess all polynomial moments we establish parametric conditions which guarantee existence and differentiability of transition densities of affine models and show how they naturally fit into the approximation framework empirical applications in option pricing credit risk and likelihood inference highlight the usefulness of our expansions the approximations are extremely fast to evaluate and they perform very accurately and numerically stable

kernel induced fuzzy clustering of image pixels with an improved differential evolution algorithm a modified differential evolution de algorithm is presented for clustering the pixels of an image in the gray scale intensity space the algorithm requires no prior information about the number of naturally occurring clusters in the image it uses a kernel induced similarity measure instead of the conventional sum of squares distance use of the kernel function makes it possible to partition data that is linearly non separable and non hyper spherical in the original input space into homogeneous groups in a transformed high dimensional feature space a novel search variable representation scheme is adopted for selecting the optimal number of clusters from several possible choices extensive performance comparison over a test suite of 10 gray scale images and objective comparison with manually segmented ground truth indicates that the proposed algorithm has an edge over a few state of the art algorithms for automatic multi class image segmentation

on the free vibration analysis of spinning structures by using discrete or distributed mass models abstract the analysis of the natural frequencies and modes of vibration of a spinning structure is in general complicated by the presence of gyroscopic or coriolis forces leading to a complex hermitian dynamic stiffness or impedance matrix it is shown that if the analysis is performed by using a discrete model with n degrees of freedom the leading principal minors of the n th order dynamic stiffness matrix exhibit a type of sturm sequence property this leads to a theorem which can be used for the systematic calculation of the natural frequencies of either a discrete system which is assembled from sub structures or an assembly of distributed mass members in both of these cases the order n of the matrix is less than the number of degrees of freedom and its determinant possesses poles as well as zeros the theorem is identical with a corresponding one for non spinning structures previously derived by the authors the application of the theorem to a spinning two dimensional frame with distributed mass members is discussed in some detail

a decentralized quantum inspired particle swarm optimization algorithm with cellular structured population this paper proposes a decentralized form of quantum inspired particle swarm optimization qpso with cellular structured population called cqpso for keeping the population diversity and balancing the global and local search the cqpso is further improved by re designing the local attractor in the sub population called cqpso lbest in order to accelerate the diffusion of the best solution and thus enhance the performance of cqpso the particles in cqpso and cqpso lbest are distributed in a two dimensional 2d grid and only allowed to interact with their neighbors according to the specified neighborhood which plays a role in exploiting the search space inside the neighborhood the overlapping particles work for delivering the information among the nearest neighborhoods acting as exploring the search space with diffusion of solutions during the evolutionary process theoretical studies are made to analyze the global convergence of cpso and cqpso lbest based on the theory of probabilistic metric space we systematically investigate the performance of cqpso lbest on 42 benchmark functions with different properties including unimodal multimodal separated shifted rotated noisy and mis scaled and compare with a set of pso variants with different topologies and swarm based evolutionary algorithms eas the experimental results demonstrate the better performance of cqpso lbest moreover two real world problems which are two dimensional 2d iir digital filter design and economic dispatch ed problem from power systems area are used to evaluate cqpso lbest and the experimental results verified the advantages of cqpso lbest

effective search for pittsburgh learning classifier systems via estimation of distribution algorithms pittsburgh style learning classifier systems lcss in which an entire candidate solution is represented as a set of variable number of rules combine supervised learning with genetic algorithms gas to evolve rule based classification models it has been shown that standard crossover operators in gas do not guarantee an effective evolutionary search in many sophisticated problems that contain strong interactions between features in this paper we propose a pittsburgh style learning classifier system based on the bayesian optimization algorithm with the aim of improving the effectiveness and efficiency of the rule structure exploration in the proposed method classifiers are generated and recombined at two levels at the lower level single rules contained in classifiers are produced by sampling bayesian networks which characterize the global statistical information extracted from the current promising rules in the search space at the higher level classifiers are recombined by rule wise uniform crossover operators to keep the semantics of rules in each classifier experimental studies on both artificial and real world binary classification problems show that the proposed method converges faster while achieving solutions with the same or even higher accuracy compared with the original pittsburgh style lcss

exact simulation of point processes with stochastic intensities point processes with stochastic arrival intensities are ubiquitous in many areas including finance insurance reliability health care and queuing they can be simulated from a poisson process by time scaling with the cumulative intensity the paths of the cumulative intensity are often generated with a discretization method however discretization introduces bias into the simulation results the magnitude of the bias is difficult to quantify this paper develops a sampling method that eliminates the need to discretize the cumulative intensity the method is based on a projection argument and leads to unbiased simulation estimators it is exemplified for a point process whose intensity is a function of a jump diffusion process and the point process itself in this setting the method facilitates the exact sampling of both the point process and the driving jump diffusion process numerical experiments demonstrate the effectiveness of the method

exact simulation of the sabr model the stochastic alpha beta rho sabr model becomes popular in the financial industry because it is capable of providing good fits to various types of implied volatility curves observed in the marketplace however no analytical solution to the sabr model exists that can be simulated directly this paper explores the possibility of exact simulation for the sabr model our contribution is threefold i we propose an exact simulation method for the forward price and its volatility in two special but practically interesting cases i e when the elasticity 1 or when 1 and the price and volatility processes are instantaneously uncorrelated primary difficulties involved are how to simulate two random variables whose distributions can be expressed in terms of the hartman watson and the noncentral chi squared distribution functions respectively two novel simulation schemes are proposed to achieve numerical accuracy efficiency and stability one stems from numerical laplace inversion and asian option literature and the other is based on recent developments in evaluating the noncentral chi squared distribution functions in a robust way numerical examples demonstrate that our method is fast and accurate under various market environments ii when 1 but the price and volatility processes are correlated our simulation method becomes a semi exact one numerical results suggest that it is still quite accurate when the time horizon is not long e g no greater than one year for long time horizons a piecewise semi exact simulation scheme is developed that reduces the biases substantially iii for european option pricing under the sabr model we propose a conditional simulation method which reduces the variance of the plain simulation significantly e g by more than 99 the e companion is available at https doi org 10 1287 opre 2017 1617

a hybrid coevolutionary algorithm for designing fuzzy classifiers rule learning is one of the most common tasks in knowledge discovery in this paper we investigate the induction of fuzzy classification rules for data mining purposes and propose a hybrid genetic algorithm for learning approximate fuzzy rules a novel niching method is employed to promote coevolution within the population which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run in order to improve the quality of the learned rules a local search method was devised to perform fine tuning on the offspring generated by genetic operators in each generation after the ga terminates a fuzzy classifier is built by extracting a rule set from the final population the proposed algorithm was tested on datasets from the uci repository and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods

ensemble strategies with adaptive evolutionary programming mutation operators such as gaussian levy and cauchy have been used with evolutionary programming ep according to the no free lunch theorem it is impossible for ep with a single mutation operator to outperform always for example classical ep cep with gaussian mutation is better at searching in a local neighborhood while the fast ep fep with the cauchy mutation performs better over a larger neighborhood motivated by these observations we propose an ensemble approach where each mutation operator has its associated population and every population benefits from every function call this approach enables us to benefit from different mutation operators with different parameter values whenever they are effective during different stages of the search process in addition the recently proposed adaptive ep aep using gaussian acep and cauchy afep mutations is also evaluated in the aep the strategy parameter values are adapted based on the search performance in the previous few generations the performance of ensemble is compared with a mixed mutation strategy which integrates several mutation operators into a single algorithm as well as against the aep with a single mutation operator improved performance of the ensemble over the single mutation based algorithms and mixed mutation algorithm is verified using statistical tests

parallel memetic structures memetic computing mc structures are algorithms composed of heterogeneous operators memes for solving optimization problems in order to address these problems this study investigates and proposes a simple yet extremely efficient structure namely parallel memetic structure pms pms is a single solution optimization algorithm composed of tree operators the first one being a stochastic global search which explores the entire decision space searching for promising regions in analogy with electrical networks downstream of the global search component there is a parallel of two alternative elements i e two local search algorithms with different features in terms of search logic whose purpose is to refine the search in the regions detected by the upstream element the first local search explores the space along the axes while the second performs diagonal movements in the direction of the estimated gradient the pms algorithm despite its simplicity displays a respectable performance compared to that of popular meta heuristics and modern optimization algorithms representing the state of the art in the field thanks to its simple structure pms appears to be a very flexible algorithm for various problem features and dimensionality values unlike modern complex algorithm that are specialized for some benchmarks and some dimensionality values pms achieves solutions with a high quality in various and diverse contexts for example both on low dimensional and large scale problems an application example in the field of magnetic sensors further proves the potentials of the proposed approach this study confirms the validity of the ockham s razor in mc efficiently designed simple structures can perform as well as if not better than complex algorithms composed of many parts

inverse iteration for the quadratic eigenvalue problem the inverse iteration method for the linear eigenvalue problem is extended to the quadratic eigenvalue problem based on the famous newtonian iteration for algebraic equations the process is shown to converge to the desired modes by means of a newly introduced continuation parameter for highly non conservative systems only physical co ordinates are involved and the sparsity of the original matrices is preserved to achieve high computational efficiency the required orthonormalization condition and the generalized rayleigh s quotient are given the method can handle complex eigensolution without difficulties

boundary preserving semianalytic numerical algorithms for stochastic differential equations construction of splitting step methods and properties of related nonnegativity and boundary preserving semianalytic numerical algorithms for solving stochastic differential equations sdes of ito type are discussed as the crucial assumption we oppose conditions such that one can decompose the original system of sdes into subsystems for which one knows either the exact solution or its conditional transition probability we present convergence proofs for a newly designed splitting step algorithm and simulation studies for numerous well known numerical examples ranging from stochastic dynamics occurring in asset pricing in mathematical finance cox ingersoll ross cir and constant elasticity of variance cev models to measure valued diffusion and super brownian motion stochastic pdes spdes as met in biology and physics

pricing options in jump diffusion models an extrapolation approach we propose a new computational method for the valuation of options in jump diffusion models the option value function for european and barrier options satisfies a partial integrodifferential equation pide this pide is commonly integrated in time by implicit explicit imex time discretization schemes where the differential diffusion term is treated implicitly while the integral jump term is treated explicitly in particular the popular imex euler scheme is first order accurate in time second order accuracy in time can be achieved by using the imex midpoint scheme in contrast to the above approaches we propose a new high order time discretization scheme for the pide based on the extrapolation approach to the solution of odes that also treats the diffusion term implicitly and the jump term explicitly the scheme is simple to implement can be added to any pide solver based on the imex euler scheme and is remarkably fast and accurate we demonstrate our approach on the examples of merton s and kou s jump diffusion models the diffusion extended variance gamma model as well as the two dimensional duffie pan singleton model with correlated and contemporaneous jumps in the stock price and its volatility by way of example pricing a one year double barrier option in kou s jump diffusion model our scheme attains accuracy of 10 5 in 72 time steps in 0 05 seconds in contrast it takes the first order imex euler scheme more than 1 3 million time steps in 873 seconds and the second order imex midpoint scheme 768 time steps in 0 49 seconds to attain the same accuracy our scheme is also well suited for bermudan options combining simplicity of implementation and remarkable gains in computational efficiency we expect this method to be very attractive to financial engineering modelers

the law of the euler scheme for stochastic differential equations i convergence rate of the distribution function we study the approximation problem ofef x t byef x t n where x t is the solution of a stochastic differential equation x t n is defined by the euler discretization scheme with stept n andf is a given function for smoothf s talay and tubaro have shown that the erroref x t f x t n can be expanded in powers of 1 n which permits to construct romberg extrapolation precedures to accelerate the convergence rate here we prove that the expansion exists also whenf is only supposed measurable and bounded under an additional nondegeneracy condition of hormander type for the infinitesimal generator of x t to obtain this result we use the stochastic variations calculus in the second part of this work we will consider the density of the law ofx t n and compare it to the density of the law ofx t

an algorithm for matrix polynomial eigenproblems abstract when a system is described by higher order differential equations with time as the independent variable the solution for the homogeneous system is defined by a matrix polynomial eigenproblem a method alternative to the classical companion matrix method is introduced to expand the determinant algebraically to result in a scalar polynomial equation for the eigenvalues the eigenvectors are obtained by inverse iteration it is shown that the new method is computationally more advantageous than the conventional companion matrix method a computer program is given for general matrix polynomials as well as hermitian matrix polynomials defective eigenproblems can be handled without special attention

high order discretization schemes for the cir process application to affine term structure and heston models this paper presents weak second and third order schemes for the cox ingersoll ross cir process without any restriction on its parameters at the same time it gives a general recursive construction method to get weak second order schemes that extends the one introduced by ninomiya and victoir cite nv combining these both results this allows to propose a second order scheme for more general affine diffusions simulation examples are given to illustrate the convergence of these schemes on cir and heston models algorithms are stated in a pseudocode language

ensemble of niching algorithms although niching algorithms have been investigated for almost four decades as effective procedures to obtain several good and diverse solutions of an optimization problem no effort has been reported on combining different niching algorithms to form an effective ensemble of niching algorithms in this paper we propose an ensemble of niching algorithms ena and illustrate the concept by an instantiation which is realized using four different parallel populations the offspring of each population is considered by all parallel populations the instantiation is tested on a set of 16 real and binary problems and compared against the single niching methods with respect to searching ability and computation time results confirm that ena method is as good as or better than the best single method in it on every test problem moreover comparison with other state of the art niching algorithms demonstrates the competitiveness of our proposed ena

a niche hybrid genetic algorithm for global optimization of continuous multimodal functions a niche hybrid genetic algorithm nhga is proposed in this paper to solve continuous multimodal optimization problems more efficiently accurately and reliably it provides a new architecture of hybrid algorithms which organically merges the niche techniques and nelder mead s simplex method into gas in the new architecture the simplex search is first performed in the potential niches which likely contain a global optimum to locate the promising zones within search space quickly and reliably then another simplex search is used to quickly discover the global optimum in the located promising zones the proposed method not only makes the exploration capabilities of gas stronger through niche techniques but also has more powerful exploitation capabilities by using simplex search so it effectively alleviates premature convergence and improves weak exploitation capacities of gas a set of benchmark functions is used to demonstrate the validity of nhga and the role of every component of nhga numerical experiments show that the nhga may efficiently and reliably obtain a more accurate global optimum for the complex and high dimension multimodal optimization problems it also demonstrates that the new hybrid architecture is potential and can be used to generate more potential hybrid algorithms

general optimized lower and upper bounds for discrete and continuous arithmetic asian options we propose an accurate method for pricing arithmetic asian options on the discrete or continuous average in a general model setting by means of a lower bound approximation in particular we derive analytical expressions for the lower bound in the fourier domain this is then recovered by a single univariate inversion and sharpened using an optimization technique in addition we derive an upper bound to the error from the lower bound price approximation our proposed method can be applied to computing the prices and price sensitivities of asian options with fixed or floating strike price discrete or continuous averaging under a wide range of stochastic dynamic models including exponential levy models stochastic volatility models and the constant elasticity of variance diffusion our extensive numerical experiments highlight the notable performance and robustness of our optimized lower bound for different test cases

eigenclassifiers for combining correlated classifiers in practice classifiers in an ensemble are not independent this paper is the continuation of our previous work on ensemble subset selection a ulas m semerci o t yildiz e alpaydin incremental construction of classifier and discriminant ensembles information sciences 179 9 2009 1298 1318 and has two parts first we investigate the effect of four factors on correlation i algorithms used for training ii hyperparameters of the algorithms iii resampled training sets iv input feature subsets simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms second we propose postprocessing before fusing using principal component analysis pca to form uncorrelated eigenclassifiers from a set of correlated experts combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination because using all allows redundancy

dynamic stiffness for structures with distributed deterministic or random loads abstract the dynamic stiffness method applies mainly to excitations of harmonic nodal forces for distributed loads modal analysis is generally required in the case of a clamped clamped beam the modal decomposition of a uniformly distributed load by the eigenbeam functions inherits slow convergence because the finite loads at the beam ends cannot be represented efficiently by the zero deflection and zero slope of the clamped clamped beam functions the computed reactions at the supports do not converge at all the problem is eliminated in this paper by using the finite element interpolation functions for the distributed load if the distributed load is adequately represented explicit exact solutions are found otherwise the residual load is expanded in the modal space as the residual modal force is much smaller and agrees well with the clamped clamped conditions fast convergence is achieved by means of the principle of superposition a structure with members having distributed loads can be analyzed by two systems one is associated with the individual members having distributed loads and the other is associated with resulting equivalent nodal forces the required frequency functions are given for all possible cases the results presented are exact if the load is interpolated adequately by finite element shape functions both deterministic and random loads are considered closed form solutions are obtained for the first time

alternative models for stock price dynamics abstract this paper evaluates the role of various volatility specifications such as multiple stochastic volatility sv factors and jump components in appropriate modeling of equity return distributions we use estimation technology that facilitates nonnested model comparisons and use a long data set which provides rich information about the conditional and unconditional distribution of returns we consider two broad families of models 1 the multifactor loglinear family and 2 the affine jump family both classes of models have attracted much attention in the derivatives and econometrics literatures there are various tradeoffs in considering such diverse specifications if pure diffusion sv models are chosen over jump diffusions it has important implications for hedging strategies if logarithmic models are chosen over affine ones it may seriously complicate option pricing comparing many different specifications of pure diffusion multifactor models and jump diffusion models we find that 1 log linear models have to be extended to two factors with feedback in the mean reverting factor 2 affine models have to have a jump in returns stochastic volatility or probably both models 1 and 2 are observationally equivalent on the data set in hand in either 1 or 2 the key is that the volatility can move violently as we obtain models with comparable empirical fit one must make a choice based on arguments other than statistical goodness of fit criteria the considerations include facility to price options to hedge and parsimony the affine specification with jumps in volatility might therefore be preferred because of the closed form derivatives prices

evolutionary multimodal optimization using the principle of locality the principle of locality is one of the most widely used concepts in designing computing systems to explore the principle in evolutionary computation crowding differential evolution is incorporated with locality for multimodal optimization instead of generating trial vectors randomly the first method proposed takes advantage of spatial locality to generate trial vectors temporal locality is also adopted to help generate offspring in the second method proposed temporal and spatial locality are then applied together in the third method proposed numerical experiments are conducted to compare the proposed methods with the state of the art methods on benchmark functions experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality further experiments are also conducted on two application problems one is the varied line spacing holographic grating design problem while the other is the protein structure prediction problem the numerical results demonstrate the effectiveness of the methods proposed

multithreaded parallel dual population genetic algorithm mpdpga for unconstrained function optimizations on multi core system various problems viz population diversity problem premature convergence problem and curse of dimensionality problem are associated with genetic algorithm ga dual population ga dpga helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population this helps to solve the problem of premature convergence and helps in early convergence of the algorithm the binary encoded multithreaded parallel dpga mpdpga is proposed in this paper to solve the problems of population diversity and premature convergence the experimental results show that the performance mean standard deviation and standard error of mean student t test mean function evaluation and success rate of mpdpga is better than serial dpga sdpga and simple ga sga

accuracy of maximum likelihood parameter estimators for heston stochastic volatility sde we study approximate maximum likelihood estimators mles for the parameters of the widely used heston stock price and volatility stochastic differential equations sdes we compute explicit closed form estimators maximizing the discretized log likelihood of n observations recorded at times t 2t ldots nt we compute the asymptotic biases of these parameter estimators for t fixed and n rightarrow infty as well as the rate at which these biases vanish when t rightarrow 0 we determine asymptotically consistent explicit modifications of these mles for the heston volatility sde we identify a canonical form determined by two canonical parameters 0 1 2 which are explicit functions of the original sde parameters we analyze theoretically the asymptotic distribution of the mles and of their consistent modifications and we outline their concrete speeds of convergence by numerical simulations we clarify in terms of zeta the precise dichotomy between asymptotic normality and attraction by stable like distributions with heavy tails we illustrate numerical model fitting for heston sdes by two concrete examples one for daily data and one for intraday data both with moderate values of n

multi objective evolutionary algorithms based on the summation of normalized objectives and diversified selection most multi objective evolutionary algorithms moeas use the concept of dominance in the search process to select the top solutions as parents in an elitist manner however as moeas are probabilistic search methods some useful information may be wasted if the dominated solutions are completely disregarded in addition the diversity may be lost during the early stages of the search process leading to a locally optimal or partial pareto front beside this the non domination sorting process is complex and time consuming to overcome these problems this paper proposes multi objective evolutionary algorithms based on summation of normalized objective values and diversified selection snov ds the performance of this algorithm is tested on a set of benchmark problems using both multi objective evolutionary programming moep and multi objective differential evolution mode with the proposed method the performance metric has improved significantly and the speed of the parent selection process has also increased when compared with the non domination sorting in addition the proposed algorithm also outperforms ten other algorithms

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

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