Dual Attractive Centers Optimization: A Simple and Efficient Approach for Real Function

A new global optimization called dual attractive centers optimization (DACO) for optimizing possibly nonlinear and non-differentiable continuous space function is proposed. DACO has two attractive centers, the best attractive center and a random attractive center. The best one is for local search, and the random one for global search. The algorithm’s performance was studied using a test bed of real valued functions with different degrees. In all cases studied, DACO performed very well. The new method requires only two control variables, population size and mutation probability, easy to use.

[1]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  Maoguo Gong,et al.  Adaptive chaos clonal evolutionary programming algorithm , 2007, Science in China Series F: Information Sciences.

[3]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[4]  Roberto Irizarry,et al.  LARES: An Artificial Chemical Process Approach for Optimization , 2004, Evolutionary Computation.

[5]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[6]  Yongling Zheng,et al.  On the convergence analysis and parameter selection in particle swarm optimization , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[7]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[8]  René Thomsen,et al.  Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. , 2003, Bio Systems.

[9]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[10]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Du Hai-feng,et al.  Adaptive chaos clonal evolutionary programming algorithm , 2005 .

[13]  Nikolaus Hansen,et al.  Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.