Improved Whale Optimization Algorithm and Its Application to UCAV Path Planning Problem

Abstract

This study proposes an improved whale optimization algorithm (WOA), termed improved WOA, by proposing a new judgment criterion for selecting the process of encircling prey or searching for prey in the WOA. The new judgment criterion is a self-tuning parameter that is based on the quality of agent’s fitness instead of a random value used in the original WOA. The agent with higher fitness, i.e., superior agent, updates its position towards the best agent found so far. On the contrary, the agent with lower fitness, i.e., inferior agent, updates its position toward a reference agent which is selected randomly from the population. The performance of the proposed WOA is examined by testing six benchmark functions on low, medium, and high dimensions. Furthermore, the proposed WOA is applied to the path planning of unmanned combat aerial vehicle (UCAV). The computed results of flight path and optimal cost obtained using the improved WOA will be compared with those obtained using the original WOA.