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.

[1]  Ling Wu,et al.  An anytime algorithm based on modified GA for dynamic weapon-target allocation problem , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[2]  Kai Zhang,et al.  An improved discrete Shuffled Frog Leaping Algorithm for cooperative Multi-Target assignment of BVR air combat , 2014, 2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[3]  Xu Jiang-hu.  Improved MOPSO algorithm for multi-objective programming model of weapon-target assignment , 2013 .

[4]  Maoguo Gong,et al.  Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection , 2008, Evolutionary Computation.

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Liu Tian-hua.  Modeling and Optimization on Fleet Antiaircraft Firepower Allocation , 2010 .

[7]  Fan Cheng-l.  Weapon-target allocation optimization algorithm based on IDPSO , 2015 .

[8]  Wang Yanxia,et al.  Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm , 2008 .

[9]  Chou-Yuan Lee,et al.  Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[10]  D.A. Van Veldhuizen,et al.  On measuring multiobjective evolutionary algorithm performance , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[13]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.