a new meta heuristic algorithm for continuous engineering optimization harmony search theory and practice most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point these algorithms however reveal a limited approach to complicated real world optimization problems if there is more than one local optimum in the problem the result may depend on the selection of an initial point and the obtained optimal solution may not necessarily be the global optimum this paper describes a new harmony search hs meta heuristic algorithm based approach for engineering optimization problems with continuous design variables this recently developed hs algorithm is conceptualized using the musical process of searching for a perfect state of harmony it uses a stochastic random search instead of a gradient search so that derivative information is unnecessary various engineering optimization problems including mathematical function minimization and structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the hs algorithm the results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms 2004 elsevier b v all rights reserved

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

discriminant analysis by gaussian mixtures fisher rao linear discriminant analysis lda is a valuable tool for multigroup classification lda is equivalent to maximum likelihood classification assuming gaussian distributions for each class in this paper we fit gaussian mixtures to each class to facilitate effective classification in non normal settings especially when the classes are clustered low dimensional views are an important by product of lda our new techniques inherit this feature we can control the within class spread of the subclass centres relative to the between class spread our technique for fitting these models permits a natural blend with nonparametric versions of lda

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

omni optimizer a generic evolutionary algorithm for single and multi objective optimization due to the vagaries of optimization problems encountered in practice users resort to different algorithms for solving different optimization problems in this paper we suggest and evaluate an optimization procedure which specializes in solving a wide variety of optimization problems the proposed algorithm is designed as a generic multi objective multi optima optimizer care has been taken while designing the algorithm such that it automatically degenerates to efficient algorithms for solving other simpler optimization problems such as single objective uni optimal problems single objective multi optima problems and multi objective uni optimal problems the efficacy of the proposed algorithm in solving various problems is demonstrated on a number of test problems chosen from the literature because of its efficiency in handling different types of problems with equal ease this algorithm should find increasing use in real world optimization problems

two parameter tuned meta heuristics for a discounted inventory control problem in a fuzzy environment highlightsa nearly real world mp mp inventory control problem under discounts and budget constraints is investigated the required storages space is considered a fuzzy number the goal is to find the optimal ordered quantities of the products a hsa is developed to solve the complex problem in this paper a nearly real world multi product multi period inventory control problem under budget constraint is investigated where shortages in combination with backorders and lost sales are considered for each product the ordered quantities of products are delivered in batch sizes with a known number of boxes each containing a pre specified number of products some products are purchased under an all unit discount policy and others are purchased under an incremental quantity discount with fuzzy discount rates the goal is to find the optimal ordered quantities of products such that not only the total inventory cost but also the required storage space considered as a fuzzy number to store the products is minimized the weighted linear sum of objectives is applied to generate a single objective model for the bi objective problem at hand and a harmony search algorithm is developed to solve the complex inventory problem as no benchmarks are available to validate the obtained results a particle swarm optimization algorithm is employed to solve the problem in addition to validate the results given by the harmony search method the parameters of both algorithms are tuned using both taguchi and response surface methodology rsm finally to assess the performance of the proposed algorithms some numerical examples are generated and the results are compared statistically

harmony search algorithm this article presents a novel modification of the harmony search hs algorithm that is able to self tune as the search progress this adaptive behavior is independent of total iterations moreover it requires less iterations and provides more precision than other variants of hs its effectiveness and performance was assessed comparing our data against four well known and recent modifications of hs ihs improved harmony search 2007 abhs adjustable bandwidth harmony search 2014 pahs parameter adaptive harmony search 2014 and ighs intelligent global harmony search 2014 unlike other works we did not analyze the data for a given number of iterations instead we ran each algorithm until it achieved a given level of precision and analyzed the number of iterations it required our test benchmark contained 30 standard test functions distributed like this 11 unimodal 8 multimodal with fixed dimensions and 11 multimodal with variable dimensions the latter also included a function whose optima was located at a different coordinate in each dimension the search domain for each function was fixed according to the literature though we also executed tests regarding the effect of varying it we carried out a parameter sweep to find adequate values for each parameter of our proposed algorithm analyzing 100 independent runs for 648 different combinations data confirm the implemented procedure outperformed the other variants for problems in 2d and in 5d scaling the test functions to 10d 30d and 50d reduced the convergence rate of the implemented procedure but it still outperformed ihs abhs and pahs in some cases e g schwefel function in 30d the self regulated fretwidth harmony search algorithm sfhs was found to be the fastest approach it was also found that ighs performs well for optimization problems whose optima is located at the same coordinates in all dimensions but not as well in other scenarios our proposed algorithm sfhs is not hindered by this sfhs was able to achieve full convergence for a test function with optima located at different coordinates even in 50d and while exploring a search domain of 1250 1250 sfhs is able to achieve a more precise solution than hs several orders of smaller magnitude and so it stands as a good improvement over hs as well as over the tested variants ihs abhs pahs ighs still our proposed method exhibited some limitations such as requiring more parameters than other variants being unable to converge 100 of the times for all high dimensional functions and sometimes needing several iterations to converge in the first case we think this allows for more freedom in the evolution of parameters in the second case we consider it can be addressed by replicating the self tuning behavior to the remaining parameters in the final aspect we estimate that accelerating the evolution of sfhs could prove a very useful strategy regarding other optimization strategies we ran tests in up to 30 dimensions and compared our data against the firefly algorithm we found that our proposed method retains 100 convergence rate while the convergence rate of firefly drops drastically in some cases even yielding 0 at 30 dimensions

estimation of generalized mixtures and its application in image segmentation we introduce the notion of a generalized mixture and propose some methods for estimating it along with applications to unsupervised statistical image segmentation a distribution mixture is said to be generalized when the exact nature of the components is not known but each belongs to a finite known set of families of distributions for instance we can consider a mixture of three distributions each being exponential or gaussian the problem of estimating such a mixture contains thus a new difficulty we have to label each of three components there are eight possibilities we show that the classical mixture estimation algorithms expectation maximization em stochastic em sem and iterative conditional estimation ice can be adapted to such situations once as we dispose of a method of recognition of each component separately that is when we know that a sample proceeds from one family of the set considered we have a decision rule for what family it belongs to considering the pearson system which is a set of eight families the decision rule above is defined by the use of skewness and kurtosis the different algorithms so obtained are then applied to the problem of unsupervised bayesian image segmentation we propose the adaptive versions of sem em and ice in the case of blind i e pixel by pixel segmentation global segmentation methods require modeling by hidden random markov fields and we propose adaptations of two traditional parameter estimation algorithms gibbsian em gem and ice allowing the estimation of generalized mixtures corresponding to pearson s system the efficiency of different methods is compared via numerical studies and the results of unsupervised segmentation of three real radar images by different methods are presented

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

dealing with label switching in mixture models summary in a bayesian analysis of finite mixture models parameter estimation and clustering are sometimes less straightforward than might be expected in particular the common practice of estimating parameters by their posterior mean and summarizing joint posterior distributions by marginal distributions often leads to nonsensical answers this is due to the so called label switching problem which is caused by symmetry in the likelihood of the model parameters a frequent response to this problem is to remove the symmetry by using artificial identifiability constraints we demonstrate that this fails in general to solve the problem and we describe an alternative class of approaches relabelling algorithms which arise from attempting to minimize the posterior expected loss under a class of loss functions we describe in detail one particularly simple and general relabelling algorithm and illustrate its success in dealing with the label switching problem on two examples

model based gaussian and non gaussian clustering abstract the classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms including those based on the sum of squares criterion and on the criterion of friedman and rubin 1967 however as currently implemented it does not allow the specification of which features orientation size and shape are to be common to all clusters and which may differ between clusters also it is restricted to gaussian distributions and it does not allow for noise we propose ways of overcoming these limitations a reparameterization of the covariance matrix allows us to specify that some features but not all be the same for all clusters a practical framework for non gaussian clustering is outlined and a means of incorporating noise in the form of a poisson process is described an approximate bayesian method for choosing the number of clusters is given the performance of the proposed methods is studied by simulation with encouraging results the methods are applied to the analysis of a data set arising in the study of diabetes and the results seem better than those of previous analyses rh

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

an effective co evolutionary differential evolution for constrained optimization many practical problems can be formulated as constrained optimization problems due to the simple concept and easy implementation the penalty function method has been one of the most common techniques to handle constraints however the performance of this technique greatly relies on the setting of penalty factors which are usually determined by manual trial and error and the suitable penalty factors are often problem dependent and difficult to set in this paper a differential evolution approach based on a co evolution mechanism named cde is proposed to solve the constrained problems first a special penalty function is designed to handle the constraints second a co evolution model is presented and differential evolution de is employed to perform evolutionary search in spaces of both solutions and penalty factors thus the solutions and penalty factors evolve interactively and self adaptively and both the satisfactory solutions and suitable penalty factors can be obtained simultaneously simulation results based on several benchmark functions and three well known constrained design problems as well as comparisons with some existed methods demonstrate the effectiveness efficiency and robustness of the proposed method

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

reinforcement learning and evolutionary algorithms for non stationary multi armed bandit problems abstract multi armed bandit tasks have been extensively used to model the problem of balancing exploitation and exploration a most challenging variant of the mabp is the non stationary bandit problem where the agent is faced with the increased complexity of detecting changes in its environment in this paper we examine a non stationary discrete time finite horizon bandit problem with a finite number of arms and gaussian rewards a family of important ad hoc methods exists that are suitable for non stationary bandit tasks these learning algorithms that offer intuition based solutions to the exploitation exploration trade off have the advantage of not relying on strong theoretical assumptions while in the same time can be fine tuned in order to produce near optimal results an entirely different approach to the non stationary multi armed bandit problem presents itself in the face of evolutionary algorithms we present an evolutionary algorithm that was implemented to solve the non stationary bandit problem along with ad hoc solution algorithms namely action value methods with e greedy and softmax action selection rules the probability matching method and finally the adaptive pursuit method a number of simulation based experiments was conducted and based on the numerical results that we obtained we discuss the methods performances

triplet markov fields for the classification of complex structure data we address the issue of classifying complex data we focus on three main sources of complexity namely the high dimensionality of the observed data the dependencies between these observations and the general nature of the noise model underlying their distribution we investigate the recent triplet markov fields and propose new models in this class designed for such data and in particular allowing very general noise models in addition our models can handle the inclusion of a learning step in a consistent way so that they can be used in a supervised framework one advantage of our models is that whatever the initial complexity of the noise model parameter estimation can be carried out using state of the art bayesian clustering techniques under the usual simplifying assumptions as generative models they can be seen as an alternative in the supervised case to discriminative conditional random fields identifiability issues underlying the models in the nonsupervised case are discussed while the models performance is illustrated on simulated and real data exhibiting the mentioned various sources of complexity

a self adaptive combined strategies algorithm for constrained optimization using differential evolution there are a huge number of differential evolution variants that have been proposed in the literature for solving constrained problems however none of them was considered as being a well accepted approach for solving a broad range of problems with different mathematical properties therefore in this paper for a better coverage of the problem characteristics a self adaptive differential evolution algorithm is introduced to do that it uses multiple search operators in conjunction with multiple constraint handling techniques the need for such an approach is justified by experimental analysis on a well known set of problems the results show that the proposed algorithm is superior to other state of the art 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

the comparison of the constrained and unconstrained frequency domain block lms adaptive algorithms convergence properties of the constrained and the unconstrained frequency domain block lms algorithms are analyzed comparisons based on both a theoretical analysis and computer simulation are given it is shown that the unconstrained algorithm has a slower convergence rate and smaller stable range of step size than that of the constrained algorithm

simultaneous motion detection and background reconstruction with a conditional mixed state markov random field in this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction an accurate estimation of the background is only possible if we locate the moving objects meanwhile a correct motion detection is achieved if we have a good available background model the key of our joint approach is to define a single random process that can take two types of values instead of defining two different processes one symbolic motion detection and one numeric background intensity estimation it thus allows to exploit the spatio temporal interaction between a decision motion detection and an estimation intensity reconstruction problem consequently the meaning of solving both tasks jointly is to obtain a single optimal estimate of such a process the intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so called mixed state statistical framework which is extended here to account for symbolic states and conditional random fields experiments on real sequences and comparisons with existing motion detection methods support our proposal further implications for video sequence inpainting will be also discussed

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

novel meta heuristic algorithms for clustering web documents clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web recently one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches in this paper we propose novel hybrid harmony search hs based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters by modeling clustering as an optimization problem first we propose a pure harmony search based clustering algorithm that finds near global optimal clusters within a reasonable time then we hybridize k means and harmony clustering in two ways to achieve better clustering experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms

classification of textures using gaussian markov random fields the problem of texture classification arises in several disciplines such as remote sensing computer vision and image analysis in this paper we present two feature extraction methods for the classification of textures using two dimensional 2 d markov random field mrf models it is assumed that the given m m texture is generated by a gaussian mrf model in the first method the least square ls estimates of model parameters are used as features in the second method using the notion of sufficient statistics it is shown that the sample correlations over a symmetric window including the origin are optimal features for classification simple minimum distance classifiers using these two feature sets yield good classification accuracies for a seven class problem

an iterative gibbsian technique for reconstruction of m ary images abstract the reconstruction of m ary images corrupted by independent noise is treated the original image is modeled by a markov random field mrf whose parameters are unknown likewise the probabilistic structure of the noise is unknown this paper presents an iterative procedure which performs the parameter estimation and image reconstruction tasks at the same time the procedure that we call gibbsian em algorithm is a generalization to the mrf context of a general algorithm known as the em algorithm used to approximate maximum likelihood estimates for incomplete data problems a number of experiments are presented in the case of gaussian noise and binary noise showing that the gibbsian em algorithm is useful and effective for image reconstruction and segmentation

an ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem in this paper an ensemble of discrete differential evolution algorithms with parallel populations is presented in a single populated discrete differential evolution dde algorithm the destruction and construction dc procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator the performance of the dde algorithm is substantially affected by the parameters of dc procedure as well as the choice of crossover operator in order to enable the dde algorithm to make use of different parameter values and crossover operators simultaneously we propose an ensemble of dde edde algorithms where each parameter set and crossover operator is assigned to one of the parallel populations each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators as an application area the well known generalized traveling salesman problem gtsp is chosen where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster the experimental results show that none of the single populated variants was effective in solving all the gtsp instances whereas the edde performed substantially better than the single populated variants on a set of problem instances furthermore through the experimental analysis of results the performance of the edde algorithm is also compared against the best performing algorithms from the literature ultimately all of the best known averaged solutions for larger instances are further improved by the edde algorithm

novel derivative of harmony search algorithm for discrete design variables calculus has widespread applications in science and engineering optimization is one of its major subjects where a problem can be mathematically formulated and its optimal solution is determined by using derivatives however this calculus based derivative technique can only be applied to real valued or continuous valued functions rather than discrete valued functions while there are many situations where design variables contain not continuous values but discrete values by nature in order to consider these realistic design situations this study proposes a novel derivative for discrete design variables based on a harmony search algorithm detailed analysis shows how this new stochastic derivative works in the bench mark function and fluid transport network design hopefully this new derivative as a fundamental technology will be utilized in various science and engineering problems

unsupervised statistical segmentation of nonstationary images using triplet markov fields recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques thus a host of models have been proposed and the ones which have probably received considerable attention are the hidden markov fields hmf models this is due to their simplicity of handling and their potential for providing improved image quality although these models provide satisfying results in the stationary case they can fail in the nonstationary one in this paper we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation we propose an original approach based on the recent triplet markov field tmf model which enables one to deal with nonstationary class fields moreover the noise can be correlated and possibly non gaussian an original parameter estimation method which uses the pearson system to find the natures of the noise margins which can vary with the class is also proposed and used to perform unsupervised segmentation of such images experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones

a numerical approach to a multi objective optimal inventory control problem for deteriorating multi items under fuzzy inflation and discounting the optimal production and advertising policies for an inventory control system of deteriorating multi items under a single management are formulated with resource constraints under inflation and discounting in fuzzy environment here the deterioration of the items and depreciation of sales are at a constant rate deteriorated items are salvaged and the effect of inflation and time value of money are taken into consideration the inflation and discount rates are assumed to be imprecise and represented by fuzzy numbers these imprecise quantities are first transformed to corresponding intervals and then following interval mathematics the related objective function is changed to respective multi objective functions using utility function method ufm the multi objective problem is changed to a single objective problem here the production and advertisement rates are unknown and considered as control decision variables the production advertisement and demand rates are functions of time t the total profit which consists of the sales proceeds production cost inventory holding cost and advertisement cost is formulated as an optimal control problem and evaluated numerically using ufm and generalized reduced gradient grg technique finally numerical experiment sensitivity analysis and graphical representation are provided to illustrate the system for the present model expressions and graphical results are presented when the rates of advertisement are constant

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

discriminative random fields in this research we address the problem of classification and labeling of regions given a single static natural image natural images exhibit strong spatial dependencies and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy in this work we present discriminative random fields drfs to model spatial interactions in images in a discriminative framework based on the concept of conditional random fields proposed by lafferty et al 2001 the drfs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data the drf framework offers several advantages over the conventional markov random field mrf framework first the drfs allow to relax the strong assumption of conditional independence of the observed data generally used in the mrf framework for tractability this assumption is too restrictive for a large number of applications in computer vision second the drfs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the mrf framework third the interaction in labels in drfs is based on the idea of pairwise discrimination of the observed data making it data adaptive instead of being fixed a priori as in mrfs finally all the parameters in the drf model are estimated simultaneously from the training data unlike the mrf framework where the likelihood parameters are usually learned separately from the field parameters we present preliminary experiments with man made structure detection and binary image restoration tasks and compare the drf results with the mrf results

markov random field texture models we consider a texture to be a stochastic possibly periodic two dimensional image field a texture model is a mathematical procedure capable of producing and describing a textured image we explore the use of markov random fields as texture models the binomial model where each point in the texture has a binomial distribution with parameter controlled by its neighbors and number of tries equal to the number of gray levels was taken to be the basic model for the analysis a method of generating samples from the binomial model is given followed by a theoretical and practical analysis of the method s convergence examples show how the parameters of the markov random field control the strength and direction of the clustering in the image the power of the binomial model to produce blurry sharp line like and blob like textures is demonstrated natural texture samples were digitized and their parameters were estimated under the markov random field model a hypothesis test was used for an objective assessment of goodness of fit under the markov random field model overall microtextures fit the model well the estimated parameters of the natural textures were used as input to the generation procedure the synthetic microtextures closely resembled their real counterparts while the regular and inhomogeneous textures did not

on the statistical analysis of dirty pictures may 7th 1986 professor a f m smith in the chair summary a continuous two dimensional region is partitioned into a fine rectangular array of sites or pixels each pixel having a particular colour belonging to a prescribed finite set the true colouring of the region is unknown but associated with each pixel there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model the aim is to reconstruct the true scene with the additional knowledge that pixels close together tend to have the same or similar colours in this paper it is assumed that the local characteristics of the true scene can be represented by a nondegenerate markov random field such information can be combined with the records by bayes theorem and the true scene can be estimated according to standard criteria however the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field thus a simple iterative method of reconstruction is proposed which does not depend on these large scale characteristics the method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field some complications including parameter estimation are discussed potential applications are mentioned briefly

steady state solution of the deficient length constrained fblms algorithm in almost all analyses of the frequency domain block least mean square fblms algorithm the length of the adaptive filter is assumed to be equal to that of the unknown system impulse response however in many practical situations where the length of the system impulse response is extremely long the adaptive filter usually works in an under modeling situation the analysis results for the sufficient length fblms algorithm are not applicable to the deficient length case in this correspondence the steady state solution of the deficient length constrained fblms algorithm is analyzed simulations illustrate the accuracy of the theoretical results

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

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

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

chaotic harmony search algorithms harmony search hs is one of the newest and the easiest to code music inspired heuristics for optimization problems like the use of chaos in adjusting note parameters such as pitch dynamic rhythm duration tempo instrument selection attack time etc in real music and in sound synthesis and timbre construction this paper proposes new hs algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the hs to get stuck on local solutions this has been done by using of chaotic number generators each time a random number is needed by the classical hs algorithm seven new chaotic hs algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions it has been detected that coupling emergent results in different areas like those of hs and complex dynamics can improve the quality of results in some optimization problems it has been also shown that some of the proposed methods have somewhat increased the solution quality that is in some cases they improved the global searching capability by escaping the local solutions

a novel differential search algorithm and applications for structure design differential search method is recently proposed to solve box constrained global optimization problems in this paper we will further extend this method to solve generalized constrained optimization problems particularly for structure design optimization problems to handle the constraints we first propose a novel dynamic s type soft threshold penalty method then the original constrained optimization problem is transformed into a sequence of unconstrained optimization problems the proposed method is mainly comprised of two steps parameter iteration and solution iteration the parameter iteration is to update the dynamic penalty parameter through a soft threshold scheme and the solution iteration is to implement differential search algorithm to solve an unconstrained optimization problem two benchmark sets cec2006 and cec2010 and four engineering structure design optimization problems are solved by our proposed algorithm as well as many other swarm based algorithms proposed in recent literatures numerical results show that our method can achieve better performance but with fewer function evaluations comparing with the existing algorithms

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

backtracking search optimization algorithm for numerical optimization problems this paper introduces the backtracking search optimization algorithm bsa a new evolutionary algorithm ea for solving real valued numerical optimization problems eas are popular stochastic search algorithms that are widely used to solve non linear non differentiable and complex numerical optimization problems current research aims at mitigating the effects of problems that are frequently encountered in eas such as excessive sensitivity to control parameters premature convergence and slow computation in this vein development of bsa was motivated by studies that attempt to develop simpler and more effective search algorithms unlike many search algorithms bsa has a single control parameter moreover bsa s problem solving performance is not over sensitive to the initial value of this parameter bsa has a simple structure that is effective fast and capable of solving multimodal problems and that enables it to easily adapt to different numerical optimization problems bsa s strategy for generating a trial population includes two new crossover and mutation operators bsa s strategies for generating trial populations and controlling the amplitude of the search direction matrix and search space boundaries give it very powerful exploration and exploitation capabilities in particular bsa possesses a memory in which it stores a population from a randomly chosen previous generation for use in generating the search direction matrix thus bsa s memory allows it to take advantage of experiences gained from previous generations when it generates a trial preparation this paper uses the wilcoxon signed rank test to statistically compare bsa s effectiveness in solving numerical optimization problems with the performances of six widely used ea algorithms pso cmaes abc jde clpso and sade the comparison which uses 75 boundary constrained benchmark problems and three constrained real world benchmark problems shows that in general bsa can solve the benchmark problems more successfully than the comparison algorithms

em procedures using mean field like approximations for markov model based image segmentation this paper deals with markov random field model based image segmentation this involves parameter estimation in hidden markov models for which one of the most widely used procedures is the em algorithm in practice difficult ies arise due to the dependence structure in the models and approximations are required to make the algorithm tractable we propose a class of algorithms in which the idea is to deal with systems of independent variables this corresponds to approximations of the pixels interactions similar to the mean field approximation it follows algorithms that have the advantage of taking the markovian structure into account while preserving the good features of em in addition this class that includes new and already known procedures is presented in a unified framework showing that apparently distant algorithms come from similar approximation principles we illustrate the algorithms performance on synthetic and real images these experiments point out the ability of our procedures to take the spatial information into account our algorithms often show significant improvement when comparing with the em algorithm applied with no account of the spatial structure and with the icm algorithm based on maximization of the pseudo likelihood and commonly used in image segmentation

hidden markov random field model selection criteria based on mean field like approximations hidden markov random fields appear naturally in problems such as image segmentation where an unknown class assignment has to be estimated from the observations at each pixel choosing the probabilistic model that best accounts for the observations is an important first step for the quality of the subsequent estimation and analysis a commonly used selection criterion is the bayesian information criterion bic of schwarz 1978 but for hidden markov random fields its exact computation is not tractable due to the dependence structure induced by the markov model we propose approximations of bic based on the mean field principle of statistical physics the mean field theory provides approximations of markov random fields by systems of independent variables leading to tractable computations using this principle we first derive a class of criteria by approximating the markov distribution in the usual bic expression as a penalized likelihood we then rewrite bic in terms of normalizing constants also called partition functions instead of markov distributions it enables us to use finer mean field approximations and to derive other criteria using optimal lower bounds for the normalizing constants to illustrate the performance of our partition function based approximation of bic as a model selection criterion we focus on the preliminary issue of choosing the number of classes before the segmentation task experiments on simulated and real data point out our criterion as promising it takes spatial information into account through the markov model and improves the results obtained with bic for independent mixture models

multiple buyer multiple vendor multi product multi constraint supply chain problem with stochastic demand and variable lead time a harmony search algorithm in this paper a multi buyer multi vendor supply chain problem is considered in which there are several products each buyer has limited capacity to purchase products and each vendor has warehouse limitation to store products in this chain the demand of each product is stochastic and follows a uniform distribution the lead time of receiving products from a vendor to a buyer is assumed to vary linearly with respect to the order quantity of the buyer and the production rate of the vendor for each product a fraction of the shortage is backordered and the rest are lost the ordered product quantities are placed in multiple of pre defined packets and there are service rate constraints for the buyers the goal is to determine the reorder points the safety stocks and the numbers of shipments and packets in each shipment of the products such that the total cost of the supply chain is minimized we show that the model of this problem is of an integer nonlinear programming type and in order to solve it a harmony search algorithm is employed to validate the solution and to compare the performance of the proposed algorithm a genetic algorithm is utilized as well a numerical illustration and sensitivity analysis are given at the end to show the applicability of the proposed methodology in real world supply chain problems

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

unconstrained frequency domain adaptive filter an unconstrained frequency domain least mean squares uflms algorithm is presented the algorithm is based on the overlap save technique used in frequency domain filtering the proposed algorithm converges to the wiener solution without the time domain constraint on the impulse response as proposed by ferrara for a large number of taps geq32 the uflms algorithm offers a significant reduction in computation another advantage is its fast convergence for highly correlated input signals the performance of the algorithm is illustrated by a simulation

gaussian parsimonious clustering models gaussian clustering models are useful both for understanding and suggesting powerful criteria banfield and raftery 1993 have considered a parametrization of the variance matrix ek of a cluster pk in terms of its eigenvalue decomposition ek lkdkakd k lk where lk defines the volume of pk dk is an orthogonal matrix which defines its orientation and ak is a diagonal matrix with determinant 1 which defines its shape this parametrization allows us to propose many general clustering criteria from the simplest one spherical cluster with equal volumes which leads to the classical k means criterion to the most complex one unknown and different volumes orientations and shapes for all clusters methods of optimization to derive the maximum likelihood estimates as well as the practical usefulness of these models are discussed we especially analyze the influence of the volumes of clusters we report monte carlo simulations and an application on stellar data which dramatically illustrated the relevance of allowing clusters to have different volumes

solving constrained optimization problems using a novel genetic algorithm a novel genetic algorithm is described in this paper for the problem of constrained optimization the algorithm incorporates modified genetic operators that preserve the feasibility of the trial solutions encoded in the chromosomes the stochastic application of a local search procedure and a stopping rule which is based on asymptotic considerations the algorithm is tested on a series of well known test problems and a comparison is made against the algorithms c somga and donlp2

an improved variant of the conventional harmony search algorithm the harmony search algorithm hs has been used for optimization in different fields and despite the relative short time it has been around it already has many variants this article presents a new modification of hs based on variable parameters which is able to yield better results than previously reported data and with the additional benefit of not requiring prior knowledge of the maximum number of iterations in this research a comparison is made with the original hs algorithm and with its improved version i e ihs finding that the proposed variants not only reduce convergence time of the algorithm but they also increase its precision some commonly used benchmark functions were used as a testing scenario and the performance of the novel approach is evaluated for an objective function in up to 1000d where it was found to converge appropriately these findings are important since they indicate that the proposed version could be used for different kinds of optimization problems thus allowing a broader use of the hs algorithm

ant colony optimization for image regularization based on a nonstationary markov modeling ant colony optimization aco has been proposed as a promising tool for regularization in image classification the algorithm is applied here in a different way than the classical transposition of the graph color affectation problem the ants collect information through the image from one pixel to the others the choice of the path is a function of the pixel label favoring paths within the same image segment we show that this corresponds to an automatic adaptation of the neighborhood to the segment form and that it outperforms the fixed form neighborhood used in classical markov random field regularization techniques the performance of this new approach is illustrated on a simulated image and on actual remote sensing images