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研究在多目标优化进化算法中引入强选择压力机制,以促使搜索群体在有效保证多样性的前提下向Pareto最优前沿迅速收敛,并引入空间超体积测度.针对当前空间超体积测度计算代价高的问题,提出了一种基于空间切片的快速空间超体积贡献计算方法FH.基于该方法,发展出一种基于快速计算空间超体积贡献机制的多目标进化算法(FH-MOEA),并应用于解决复杂的多目标优化问题.用一组测试问题对算法性能进行检验,实验结果表明,该算法在收敛性和分布性两方面均比著名的NSGA-Ⅱ算法有显著提高.
In this paper, a strong selection pressure mechanism is introduced into the multi-objective evolutionary algorithm to make the search population converge quickly to the Pareto optimal frontier under the premise of ensuring diversity, and the space overvolume measure is introduced.Aiming at the high computational cost , A new FH-MOEA method based on space slice for rapid volume hypervolume contribution is proposed. Based on this method, a multi-objective evolutionary algorithm (FH-MOEA) based on the fast hyperspace contribution mechanism is developed and applied to To solve the complex multi-objective optimization problem, a set of test questions is used to test the performance of the algorithm. Experimental results show that the proposed algorithm has significantly improved convergence and distribution compared with the well-known NSGA-Ⅱ algorithm.