关键词: 多目标优化 遗传算法 演化算法 Pareto最优

Abstract:

In multi-objective optimization genetic algorithms, the whole population is sorted with respect to the values of the focused objective function and di vided into sub-populations. In each sub-population,μ parents generate λ, offsprings by genetic operators. Then,all individuals are gathered and sorted d, the fitness sharing is performed and better individuals are selected to be the next generation. In this approach, the Pareto optimum solutions which  are close to each other are collected by one sub-population and using the （μ＋λ） selection strategy increases search efficiency. The genetic operati  ion which is performed in sub-populations is suitable for parallel processing and the fitness sharing of the whole population is conducive to maintainin g the diversity  of population as well.

Key words: （multl-objective optimization, genetic algorithm;evolutionary algorithm, Pareto optimal）