近日新发表一篇论文于国际顶级期刊IEEE Transactions on Cybernetics。

J. Shi, J. Sun, Q. Zhang, H. Zhang and Y. Fan, "Improving Pareto Local Search Using Cooperative Parallelism Strategies for Multiobjective Combinatorial Optimization," in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2022.3226744.

Abstract: Pareto local search (PLS) is a natural extension of local search for multiobjective combinatorial optimization problems (MCOPs). In our previous work, we improved the anytime performance of PLS using parallel computing techniques and proposed a parallel PLS based on decomposition (PPLS/D). In PPLS/D, the solution space is searched by multiple independent parallel processes simultaneously. This article further improves PPLS/D by introducing two new cooperative process techniques, namely, a cooperative search mechanism and a cooperative subregion-adjusting strategy. In the cooperative search mechanism, the parallel processes share high-quality solutions with each other during the search according to a distributed topology. In the proposed subregion-adjusting strategy, a master process collects useful information from all processes during the search to approximate the Pareto front (PF) and redivide the subregions evenly. In the experimental studies, three well-known NP-hard MCOPs with up to six objectives were selected as test problems. The experimental results on the Tianhe-2 supercomputer verified the effectiveness of the proposed techniques.

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9994620&isnumber=6352949