A New Compound Swarm Intelligence Algorithms for Solving Global Optimization Problems

Ibrahim M. Hezam; Osama Abdel Raouf; Mohey M. Hadhoud

doi:10.24297/ijct.v10i9.1389

Authors

Ibrahim M. Hezam Department of Mathematics & computer, Faculty of Education, Ibb University, Yemen.
Osama Abdel Raouf Department of Operations Research, Faculty of Computers & Information, Minufiya University, Egypt.
Mohey M. Hadhoud Department of Information Technology, Faculty of Computers & Information, Minufiya University, Egypt.

DOI:

https://doi.org/10.24297/ijct.v10i9.1389

Keywords:

Hybrid Swarm Intelligence, Cuckoo Search, Firefly Algorithm, Particle Swarm Optimization, Global optimization.

Abstract

This paper proposes a new hybrid swarm intelligence algorithm that encompasses the feature of three major swarm algorithms. It combines the fast convergence of the Cuckoo Search (CS), the dynamic root change of the Firefly Algorithm (FA), and the continuous position update of the Particle Swarm Optimization (PSO). The Compound Swarm Intelligence Algorithm (CSIA) will be used to solve a set of standard benchmark functions. The research study compares the performance of CSIA with that of CS, FA, and PSO, using the same set of benchmark functions. The comparison aims to test if the performance of CSIA is Competitive to that of the CS, FA, and PSO algorithms denoting the solution results of the benchmark functions.