Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV

• Authors: Yangguang Fu;Mingyue Ding;Chengping Zhou
• Venue: IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
• Year: 2012

A new variant of particle swarm optimization (PSO), named phase angle-encoded and quantum-behaved particle swarm optimization ($\theta$-QPSO), is proposed. Six versions of $\theta$-QPSO using different mappings are presented and compared through their application to solve continuous function optimization problems. Several representative benchmark functions are selected as testing functions. The real-valued genetic algorithm (GA), differential evolution (DE), standard particle swarm optimization (PSO), phase angle-encoded particle swarm optimization ( $\theta$-PSO), quantum-behaved particle swarm optimization (QPSO), and $\theta$-QPSO are tested and compared with each other on the selected unimodal and multimodal functions. To corroborate the results obtained on the benchmark functions, a new route planner for unmanned aerial vehicle (UAV) is designed to generate a safe and flyable path in the presence of different threat environments based on the $\theta$-QPSO algorithm. The PSO, $\theta$-PSO, and QPSO are presented and compared with the $\theta$-QPSO algorithm as well as GA and DE through the UAV path planning application. Each particle in swarm represents a potential path in search space. To prune the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. Experimental results demonstrated good performance of the $\theta$-QPSO in planning a safe and flyable path for UAV when compared with the GA, DE, and three other PSO-based algorithms.