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Multiobjective weapon-target assignment problem by two-stage evolutionary multiobjective particle swarm optimization

This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.