CPO: A Crow Particle Optimization Algorithm

DOI

10.2991/ijcis.2018.125905658How to use a DOI?

Keywords

Metaheuristic algorithm; Crow search algorithm; Particle swarm optimization; Function optimization; Hybridization algorithm

Abstract

Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ko-Wei Huang
AU  - Ze-Xue Wu
PY  - 2019
DA  - 2019/02/06
TI  - CPO: A Crow Particle Optimization Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 426
EP  - 435
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.125905658
DO  - 10.2991/ijcis.2018.125905658
ID  - Huang2019
ER  -