Weapon Selection and Planning Problems Using MOEA/D with Distance‐Based Divided Neighborhoods
Jian Xiong, Rui Wang, Jiang Jiang · Complexity · 2019
Real‐world multiobjective optimization problems are characterized by multiple types of decision variables. In this paper, we address weapon selection and planning problems (WSPPs), which include decision variables of weapon‐type selection and weapon amount determination. Large solution space and discontinuous, nonconvex Pareto front increase the difficulty of problem solving. This paper solves the addressed problem by means of a multiobjective evolutionary algorithm based on decomposition (MOEA/D). Two mechanisms are designed for the complex combinatorial characteristic of WSPPs. The first is that the neighborhood of each individual is divided as selection and replacement neighborhoods. The second is that the neighborhood size is changing during the evolution by introducing a distance parameter to constrain the search scope of each subproblem. The proposed algorithm is termed as MOEA/D with distance‐based divided neighborhoods (MOEA/D‐DDNs) which can overcome possible drawbacks of original MOEA/D with weighted sum approach for complex combinatorial problems. Benchmark instances are generated to verify the proposed approach. Experimental results suggest the effectiveness of the proposed algorithm.