A Chaos Mutation and Metropolis Selection Evolutionary Programming for Multi-Objective Optimization in Power System

C. Jiang (PRC)


Power System, Multi-Objective Optimization, ChaosMutation, Metropolis Selection Criteria, EvolutionaryProgramming


This paper deals with the mutation operators and the selection Criteria that affect the convergence and robustness of the evolutionary programming (EP), and suggests a chaos mutation and metropolis selection evolutionary programming method (CMMEP). Introducing chaos dynamics into mutation operators of evolutionary programming, the new method adopts the certainty method of like-stochastic to get mutation operators which breaks through the conventional thought with mutation by stochastic numbers of fixed distribution. Also, it introduces the metropolis selection of the simulated annealing in evolutionary programming to construct new selection operators. Applied to multi objective OPF, this algorithm is proved to enjoy timesaving convergence and perfect performance in the multi-objective optimization dispatch in power system.

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