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91
Ant colony optimization for continuous domains
, 2008
"... In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We presen ..."
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Cited by 72 (5 self)
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In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other continuous optimization methods: other antrelated approaches and other metaheuristics initially developed for combinatorial optimization and later adapted to handle the continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
Realcoded Memetic Algorithms with crossover hillclimbing
 Evolutionary Computation
, 2004
"... This paper presents a realcoded memetic algorithm that applies a crossover hillclimbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the cro ..."
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Cited by 71 (12 self)
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This paper presents a realcoded memetic algorithm that applies a crossover hillclimbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hillclimbing exploits the selfadaptive capacity of realparameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other realcoded memetic algorithms which appeared in the literature.
Differential Evolution Using a NeighborhoodBased Mutation Operator
, 2009
"... Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and re ..."
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Cited by 42 (8 self)
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Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and realworld problems. DE, however, is not completely free from the problems of slow and/or premature convergence. This paper describes a family of improved variants of the DE/targettobest/1/bin scheme, which utilizes the concept of the neighborhood of each population member. The idea of small neighborhoods, defined over the indexgraph of parameter vectors, draws inspiration from the community of the PSO algorithms. The proposed schemes balance the exploration and exploitation abilities of DE without imposing serious additional burdens in terms of function evaluations. They are shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions. The paper also investigates the applications of the new DE variants to two reallife problems concerning parameter estimation for frequency modulated sound waves and spread spectrum radar polyphase code design.
An Introduction to Genetic Algorithms
 Sadhana
, 1999
"... Simulated binary crossover (SBX) is a realparameter recombination operator which is commonly used in the evolutionary algorithm (EA) literature. The operator involves a parameter which dictates the spread of offspring solutions visavis that of the parent solutions. In all applications of SBX so f ..."
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Cited by 23 (1 self)
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Simulated binary crossover (SBX) is a realparameter recombination operator which is commonly used in the evolutionary algorithm (EA) literature. The operator involves a parameter which dictates the spread of offspring solutions visavis that of the parent solutions. In all applications of SBX so far, researchers have kept a fixed value throughout a simulation run. In this paper, we suggest a selfadaptive procedure of updating the parameter so as to allow a smooth navigation over the function landscape with iteration. Some basic principles of classical optimization literature are utilized for this purpose. The resulting EAs are found to produce remarkable and much better results compared to the original operator having a fixed value of the parameter. Studies on both single and multiple objective optimization problems are made with success.
Enhancing differential evolution performance with local search for high dimensional function optimization
, 2005
"... In this paper, we proposed Fittest Individual Refinement (FIR), a crossover based local search method for Differential Evolution (DE). The FIR scheme accelerates DE by enhancing its search capabilitythrough exploration of the neighborhood of the best solution in successive generations. The proposed ..."
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Cited by 23 (1 self)
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In this paper, we proposed Fittest Individual Refinement (FIR), a crossover based local search method for Differential Evolution (DE). The FIR scheme accelerates DE by enhancing its search capabilitythrough exploration of the neighborhood of the best solution in successive generations. The proposed memetic version of DE (augmented byFIR) is expected to obtain an acceptable solution with a lower number of evaluations particularlyfor higher dimensional functions. Using two different implementations DEfirDE and DEfirSPX we showed that proposed FIR increases the convergence velocityof DE for well known benchmark functions as well as improves the robustness of DE against variation of population. Experiments using multimodal landscape generator showed our proposed algorithms consistentlyoutperformed their parent algorithms. A performance comparison with reported results of well known real coded memetic algorithms is also presented.
A continuous variable neighbourhood search based on specialised EAs: Application to the noisy BBObenchmark 2009 testbed
 In Genetic Evolutionary Computation Conf
, 2009
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 23 (2 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
RMMEDA: a regularity modelbased multiobjective estimation of distribution algorithm
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2007
"... Under mild conditions, it can be induced from the Karush–Kuhn–Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewise continuous @ IAD manifold, where is the number of objectives. Based on this regularity property, we propose ..."
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Cited by 19 (3 self)
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Under mild conditions, it can be induced from the Karush–Kuhn–Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewise continuous @ IAD manifold, where is the number of objectives. Based on this regularity property, we propose a regularity modelbased multiobjective estimation of distribution algorithm (RMMEDA) for continuous multiobjective optimization problems with variable linkages. At each generation, the proposed algorithm models a promising area in the decision space by a probability distribution whose centroid is a @ IAD piecewise continuous manifold. The local principal component analysis algorithm is used for building such a model. New trial solutions are sampled from the model thus built. A nondominated sortingbased selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, RMMEDA outperforms three other stateoftheart algorithms, namely, GDE3, PCXNSGAII, and MIDEA, on a set of test instances with variable linkages. We have demonstrated that, compared with GDE3, RMMEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages. A few shortcomings of RMMEDA have also been identified and discussed in this paper.
Multiobjective test problems, linkages, and evolutionary methodologies
 in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation GECCO’06
"... Existing test problems for multiobjective optimization are criticized for not having adequate linkages among variables. In most problems, the Paretooptimal solutions correspond to a fixed value of certain variables and diversityof solutions comes mainlyfrom a random variation of certain other vari ..."
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Cited by 16 (2 self)
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Existing test problems for multiobjective optimization are criticized for not having adequate linkages among variables. In most problems, the Paretooptimal solutions correspond to a fixed value of certain variables and diversityof solutions comes mainlyfrom a random variation of certain other variables. In this paper, we introduce explicit linkages among variables so as to develop difficult two and multiobjective test problems along the lines of ZDT and DTLZ problems. On a number of such test problems, this paper compares the performance of a number of EMO methodologies having (i) variablewise versus vectorwise recombination operators and (ii) spatial versus unidirectional recombination operators. Interesting and useful conclusions on the use of above operators are made from the study.
Orthogonal Learning Particle Swarm Optimization
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2010
"... Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when sear ..."
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Cited by 15 (1 self)
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Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with
RMMEDA: A Regularity Model Based Multiobjective Estimation of Distribution Algorithm
, 2008
"... Under mild conditions, it can be induced from the KarushKuhnTucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is (m − 1)D piecewise continuous, where m is the number of objectives. Based on this regularity property, we propose a Regul ..."
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Cited by 14 (8 self)
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Under mild conditions, it can be induced from the KarushKuhnTucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is (m − 1)D piecewise continuous, where m is the number of objectives. Based on this regularity property, we propose a Regularity Model based Multiobjective Estimation of Distribution Algorithm (RMMEDA) for continuous multiobjective optimization problems with variable linkages. At each generation, the proposed algorithm models a promising area in the decision space by a probability distribution whose centroid is a (m−1)D piecewise continuous manifold. The Local Principal Component Analysis algorithm is used for building such a model. New trial solutions are sampled from the model thus built. A nondominated sorting based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, RMMEDA outperforms three other stateoftheart algorithms, namely, GDE3, PCXNSGAII and MIDEA, on a set of test instances with variable linkages. We have demonstrated that, compared with GDE3, RMMEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages. A few shortcomings of RMMEDA have also been identified and discussed in this paper.