A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

Abstract

Uninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

Keywords

• glowworm swarm optimization (gso)
• particle glowworm swarm optimization (pgso)
• particle swarm optimization (pso)
• path planning for uninhabited combat air vehicle (ucav)