Population Climbing Evolutionary Algorithm for Multimodal Function Global Optimization

• Published in: Lecture notes in computer science pp. 553 - 559
• Main Authors: Ziyi, Chen, Lishan, Kang
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Flows in networks. Combinatorial problems; Operational research and scientific management; Operational research. Management science; Local search; Genetic algorithm; Evolutionary algorithm; Optimal solution; Control program; Global optimum; Climbing behavior; Combinatorial optimization; Attractor; Artificial intelligence; Global solution
• ISBN: 9783540473312; 3540473319
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11903697_70

This paper presents a population climbing evolutionary algorithm (PCEA) for solving function optimization containing multiple global optima. The algorithm combines a multi-parent crossover operator with the complete local search. The multi-parent crossover operator can enables individual to draw closer to each optimal solution,thus the population will be divided into subpopulations automatically , meanwhile, the local search is adopted to enable individual to converge to the nearest optimal solution which belongs to the same attractor. By this way, each individuals can converge to a global optima, then the population can maintain all global optima. Comparing with other algorithms, it has the following advantages.(1) The algorithm is very simple with little computation complexity .(2) Proposed algorithm needs no additional control parameter which depends on a special problem. The experiment results show that PCEA is very efficient for the optimization of multimodal functions, usually it can obtain all the global optimal solutions by running once of the algorithm.