混合分散搜索的进化多目标优化算法

在进化多目标优化算法中，种群的多样性、对目标空间的搜索能力及算法的鲁棒性直接影响算法的收敛能力和解集的分散性。针对这些问题，提出了一种混合分散搜索的进化多目标优化算法(SSMOEA)。SSMOEA在混合分散搜索算法架构的同时，重新设计其多样性的选取策略，并引入协同进化机制。此外，为了提高算法的自适应性和鲁棒性，采用了一种新颖的自适应多交叉算子选择方法。SSMOEA与经典的多目标进化算法SPEA2、NSGA-Ⅱ和MOEA/D在12个基准测试函数上的对比结果表明，SSMOEA不仅在求得的Pareto最优解集的宽广性、均匀性和逼近性上有明显优势，而且算法的鲁棒性也有明显的提高。

Evolutionary multi-objective optimization algorithm of hybrid scatter search

The diversity of population, the searching capability and the robustness are three key points to the multi-objective optimization problem, which directly affect the convergence of algorithm and the spread of solutions set. To better deal with above problems, a Scatter Search hybrid Multi-Objective Evolutionary optimization Algorithm (SSMOEA) was proposed. The SSMOEA followed the scatter search structure but designed a new selection strategy of diversity and integrated the method of co-evolution in the process of subset generation. Additionally, a novel adaptive multi-crossover operation was employed to improve the self-adaptability and robustness of the algorithm. The experimental results on twelve standard benchmark problems show that, compared with three state-of-the-art multi-objective optimizers, SPEA2, NSGA-Ⅱ and AbYSS, SSMOEA outperforms the other three algorithms as regards the coverage, uniformity and approximation. Meanwhile, its robustness is also significantly improved.