Pursuit‐Escape Particle Swarm Optimization
Mitsuharu Higashitani, Atsushi Ishigame, Keiichiro Yasuda · IEEJ Transactions on Electrical and Electronic Engineering · 2007
Abstract This paper presents a new Particle Swarm Optimization (PSO) with pursuit and escape behavior. This method takes a cue from the behaviors of schools of sardines and pods of killer whales. When the sardines are attacked by the killer whales, they would behave unusually, that is, the sardines would escape from the killer whales, and on another front, the killer whales would pursue the sardines. By this method, particles are divided into two categories called the pursuit‐particles and the escape‐particles, having interactions with each other. They play the key roles of intensification and diversification, respectively. This allows the particles to avoid local optimal solutions and find a global optimal one, and also achieve an appropriate balance between diversification (global search) and intensification (local search) during the search. Then, the proposed method is validated through numerical simulations using several functions which are well‐known as the optimization benchmark problems by comparing them to powerful methods such as SAPPO, LDIWM, and CFM. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.