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.%The method for incorporating strong selection pressure was introduced into multi-objective evolutionary optimization algorithms (MOEAs) to force the evolution population approaches rapidly towards the Pareto optimal front with a spread as uniform as possible over the Pareto front. An effective measure called "hyper-volume contribution" was adopted to provide the strong selection pressure. Based on the fast method for calculating hyper-volume contribution proposed, a new multi-objective optimization evolutionary algorithm multi-objective evolutionary algorithm based on fast hyper-volume contribution (FH-MOEA) was proposed for the complex multi-objective optimization problem (MOP) tasks. Via a suite of designed experiments, it is distinctly indicated that FH-MOEA has a great advantage over the famous MOEA "NSGA-Ⅱ" in terms of both convergence and diversity.
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