基于自适应算子的多智能体遗传算法

2012 Fifth International Conference on Intelligent Networks and Intelligent Systems Pub Date : 2012-11-01 DOI:10.1109/ICINIS.2012.25

Lianshuan Shi, Huahui Wang

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引用次数: 1

摘要

交叉率和突变率是多目标进化算法的两个重要参数。将智能体技术应用于多目标问题的求解。提出了一种基于自适应智能体(SAMOGA)的多目标遗传算法，该算法在进化过程中自适应调整进化参数，并使用新的选择算子对个体进行选择。将该算法应用于多个多目标测试函数，仿真结果表明，该算法能够快速收敛到Pareto解，且与NSGA-II相比具有较好的多样性。

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Multi-agent Genetic Algorithm Based on Self-Adaptive Operator

The crossover and mutation rates are two important parameters of multi-objective evolutionary algorithm. The agent technology is applied to solve multi-objective problem. A new multi-objective genetic algorithm based on self-adaptive agent (SAMOGA) is proposed, in which the evolution parameters is adjusted adaptively in the evolutionary process and a new selection operator is used to select individual. The algorithm is applied to several multi-objective test functions, the simulation results show that the algorithm can converge to the Pareto solutions quickly, and has a well diversity compared with NSGA-II.

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2012 Fifth International Conference on Intelligent Networks and Intelligent Systems

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