Domain Decomposition Evolutionary Algorithm for Multi-Modal Function Optimization

In book: Advances in Evolutionary Algorithms
Source: InTech


We here proposed some self-adaptive methods to choose the results of Gaussian and Cauchy mutation, and the dimension of subspace. We used the better of Gaussian and Cauchy mutation to do local search in subspace, and used multi-parents crossover to exchange their information to do global search, and used the worst individual eliminated selection strategy to keep population more diversity. Judging by the results obtained from the above numerical experiments, we conclude that our new algorithm is both universal and robust. It can be used to solve function optimization problems with complex constraints, such as NLP problems with inequality and (or) equality constraints, or without constraints. It can solve 0-1 NLP problems, integer NLP problems and mixed integer NLP problems. When confronted with different types of problems, we don't need to change our algorithm. All that is needed is to input the fitness function, the constraint expressions, and the upper and lower limits of the variables of the problem. Our algorithm usually finds the global optimal value. In the paper we analyze the character of the multi-parent genetic algorithm, when applied to solve the optimization of multi-modal function, MPGA works in different forms during different phases and then forms two-phase genetic algorithm. The experiments indicate that DDEA is effective to solve the optimization of multi-modal function whose dimension is no

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Available from: Guangming Lin, May 19, 2014
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