A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon-Target Assignment

Guangyuan Fu,Hongqiao Wang,Jiufen Zhao,Chao Wang,Daqiao Zhang

doi:10.1155/2019/1424590

Guangyuan Fu, Hongqiao Wang + Show 3 more

Open Access

https://doi.org/10.1155/2019/1424590

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Abstract

Weapon-target assignment (WTA) is critical to command and decision making in modern battlefields and is a typical nondeterministic polynomial complete problem. To solve WTA problems with multiple optimization objectives, a multipopulation coevolution-based multiobjective particle swarm optimization (MOPSO) algorithm is proposed to realize the rapid search for the globally optimal solution. The algorithm constructs a master-slave population coevolution model. Each slave population corresponds to an objective function and is used to search for noninferior solutions. The master population receives all the noninferior solutions from the slave populations, repairs the gaps between the noninferior solutions, and generates a relatively optimal Pareto optimal solution set. In addition, to accelerate the slave populations searching for noninferior solutions and master population repairing the gaps between noninferior solutions, the particle velocity update method is improved. The simulation results show that the proposed algorithm has higher computational efficiency and achieves better solutions than existing algorithms capable of providing a good solution. The method is suitable for rapidly solving multiobjective WTA (MOWTA) problems.

Highlights

Weapon-target assignment (WTA) [1, 2] is critical to operational command and directly affects the progression and outcome of operations
According to the number of optimization objectives in the problem, WTA problems can be divided into single-objective WTA (SOWTA) problems, with only one optimization objective, and multiobjective WTA (MOWTA) problems, with at least two optimization objectives
For the dynamic SOWTA problem, Cho et al [6] constructed a static SOWTA model with several constraints and designed an improved greedy algorithm with phased optimization, which effectively improved the optimization speed of the problem, but it was still solving a static SOWTA problem; Mei et al [7] constructed a dynamic SOWTA model based on the killing region of weapon platform and proposed a combinatorial algorithm derived from heuristic algorithm and receding horizon control

Summary

Introduction

Weapon-target assignment (WTA) [1, 2] is critical to operational command and directly affects the progression and outcome of operations. Researchers have introduced operations such as random mutation [15] and stochastic perturbation [16] into the PSO algorithm or adjusted its parameters (e.g., acceleration factors [17] and inertia weights [18]) They maintain satisfactory population diversity and improve the global search capability of the algorithm, these improvement strategies lack an effective theoretical basis for parameter adjustment and must rely on simulation experiments, which significantly weakens their adaptability. Xin et al [23] improved the damage probability model of Wang et al and modelled the damage probability as the product of the weapon’s probability of kill and the sensor’s probability of detection They proposed a marginalreturn-based constructive heuristic (MRBCH) algorithm, which achieved good optimization results.

Theoretical Basis

The MPC-MOPSO Algorithm

Simulation and Analysis of the MOWTA Problem

C2 C3 C4 C5

Conclusion