A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

Guang Peng,Dandan Yang,Shaohua Chen,Weishi Peng,Yangwang Fang

doi:10.1155/2017/8063767

Abstract

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

Highlights

The weapon target assignment (WTA) is a typical NPcomplete constrained combinatorial optimization problem [1], which can be classified into two categories: static WTA (SWTA) and dynamic WTA (DWTA) [2, 3]
This paper has presented a new hybrid multiobjective DPSO called hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm to solve the cooperative air combat DWTA multiobjective optimization problems
The proposed algorithm has three advantages. (a) The leader particle selecting operator and neighborhood searching operator can improve the search ability and the rate of convergence. (b) The repairing operator can enhance the efficiency of generating feasible solutions. (c) The Cauchy mutation operator can boost the diversity and distribution of Pareto optimal solutions

Summary

Introduction

The weapon target assignment (WTA) is a typical NPcomplete constrained combinatorial optimization problem [1], which can be classified into two categories: static WTA (SWTA) and dynamic WTA (DWTA) [2, 3]. Khosla [5] proposed a hybrid approach, which combines genetic algorithm (GA) with simulated annealing (SA) to solve a target-based DWTA problem. Liu et al [12] and Zhou et al [13] proposed an improved MOPSO algorithm to solve the multiobjective programming model of SWTA, respectively. In order to meet the real-time performance and the convergence accuracy simultaneously, this paper proposed an efficient HMODPSO algorithm to solve the DWTA multiobjective optimization problem. The proposed HMODPSO algorithm can generate obviously better DWTA decisions without the cost of overmuch extra computation time, which can improve the cooperative air combat effectiveness.

The Cooperative Air Combat Multiobjective Optimization Model for DWTA

HMODPSO Algorithm for DWTA

Simulations and Results

Conclusions