快速多目标约束进化算法及其收敛性

MA Yong-jie , BAI Yu-long , JIANG Zhao-yuan
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引用次数: 11

摘要

针对约束多目标优化问题的收敛速度慢和易沉降早熟问题,提出了一种新的约束多目标优化快速进化算法。设计了一种从可行解空间和不可行解空间同时搜索的交叉算子。结合约束条件和目标,提出了一种新的个体比较的偏序关系。在此基础上,提出了一种保持种群多样性的生态位计算方法,避免了利用搜索解空间进行重复搜索。在此基础上,提出了一种有效的全局优化进化算法,并证明了该算法的收敛性。仿真结果表明,该算法能够快速收敛于全局Pareto解,并能保持种群的多样性。
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Fast Multi-objective Constrained Evolutionary Algorithm and Its Convergence

Aimed at the problems of slow pace of convergence and easy subsidence precocious problem, a new fast evolution algorithm is proposed for constrained multiobjective optimization problems. A crossover operator, which searches simultaneously from feasible and infeasible solution space is designed. Combining constraint condition and objective, a new partial-order relation for comparing individual is introduced. Thus, a new Niche computation method for maintaining diversity of population is suggested and repeat search is avoided using searched solution space. Based on all these, a novel effective evolution algorithm for global optimization is proposed and its convergence is proved. Compared with the current MOEAs, the simulation results show that this algorithm can rapidly converge at global Pareto solutions, and can maintain diversity of population.

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