Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided Neighborhoods

Jian Xiong,Jiang Jiang,Rui Wang

doi:10.1155/2019/7589760

Abstract

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.

Highlights

Portfolio optimization problems and project planning problems are two classic topics in the areas of operational research and management science
A mechanism of distance-based divided neighborhood (DDN) is designed and incorporated into the multiobjective evolutionary algorithm based on decomposition (MOEA/D). e proposed algorithm is termed as multiobjective evolutionary algorithms (MOEAs)/D-DDN
The performances of MOEA/D-DivN, MOEA/D-DisN, and MOEA/D are comparable. It suggests that the combination of two algorithm components of MOEA/D-DDN is effective for the addressed problem

Summary

Introduction

Portfolio optimization problems and project planning problems are two classic topics in the areas of operational research and management science. E former focuses on the item selection among available set, while the latter concerns activity arrangement with a time horizon Simultaneous consideration of these two types of optimization processes is rather scant in the literature. The basic optimization process of weapon development in CBP is modeled as a weapon selection and planning problem (WSPP) [5]. We re ne the WSPP model and concern the design of e ective multiobjective evolutionary algorithms to solve the problem. Components of original MOEA/D, such as neighborhood definition, selection, and replacement, might be problematic for combinatorial problems To address these issues, a mechanism of distance-based divided neighborhood (DDN) is designed and incorporated into the MOEA/D. e proposed algorithm is termed as MOEA/D-DDN.

Review of the Related Literature

Weapon Selection and Planning Problems

Genetic Representation and Operators for WSPPs

Experimental Analysis

Conclusion