Opposition-based Memetic Algorithm for the Shared Weapon Target Assignment Problem

The optimal arrangement of allocating defensive weapons to the enemy targets in order to obtain the maximized military effectiveness is a weapon target assignment problem. We consider a shared weapon target assignment (SWTA) scenario such that it allows different enemy targets to share subunits to simulate a real battlefield environment. To solve the SWTA, we extract the SWTA issue into a set-union knapsack problem (SUKP) model and apply an opposition-based memetic algorithm that integrates the concept of opposition-based learning mechanism into the powerful memetic algorithm framework, with a double backbone-based crossover operator for generating offspring solutions, a powerful tabu search procedure for local improvement and an effective population updating strategy for maintaining the population diversity. Experimental results on 60 benchmark instances prove that our algorithm is able to obtain all the best results reported by the best-performing algorithm with less computational resources, which shows it is more suitable for practical applications. We analyze the key components of the proposed algorithm to see its impact on the performance of the algorithm. Findings in this paper enrich the solution methods for both SUKP and SWTA.