An evolutionary programming algorithm for continuous global optimization |
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| Affiliation: | 1. Department of Information Engineering, Hiroshima University, 4-1 Kagamiyama 1 Chome 739-8527, Higashi-Hiroshima, Japan;2. Department of Mathematical Sciences, Nanzan University, Japan;3. Universite de Techologie de Compiegne, Compiegne, France;1. Department of Physics, Guilan University, Rasht, Iran;2. Department of Engineering Sciences, Sabalan University of Advanced Technologies (SUAT), Namin, Iran;3. Department of Advanced Technologies, Institute of Science and Technology, Gazi University, Ankara, Turkey;4. Department of Physics, Faculty of Sciences, Gazi University, Ankara, Turkey;5. Department of Physics, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran;1. Computational and Theoretical Chemistry Group, Departamento de Ciencias Químicas, Facultad de Ciencias Exactas, Universidad Andres Bello. República 498, Santiago, Chile;2. Facultad de Ciencias de la Salud, Universidad Autónoma de Chile. Av. Pedro de Valdivia 425, Región Metropolitana, Providencia, Chile;3. Doctorado en Fisicoquímica Molecular, Universidad Andres Bello. República 275, Santiago, Chile;4. Laboratorio de Química teórica, Facultad de Química y Biología, Universidad de Santiago de Chile (USACH). Av. Libertador Bernardo O''Higgins 3363, Estación Central, Región Metropolitana, Santiago, Chile;1. School of Environment and Safety, Taiyuan University of Science and Technology, Taiyuan, Shanxi 030024, PR China;2. Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, PR China |
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| Abstract: | Evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro-mutation (MM), local linear bisection search (LBS) and crossover operators for global optimization. The MM operator is designed to explore the whole search space and the LBS operator to exploit the neighborhood of the solution. Simulated annealing is adopted to prevent premature convergence. The performance of the proposed algorithm is assessed by numerical experiments on 12 benchmark problems. Combined with MM, the effectiveness of various local search operators is also studied. |
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