Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

Raed A Hasan; Suhail  Najm Shahab; Munef Abdullah Ahmed

doi:10.52866/ijcsm.2021.02.02.004

Authors

Raed A Hasan Electronics Department - Alhawija Technical Institute, Northern Technical University, IRAQ https://orcid.org/0000-0002-6064-0048
Suhail Najm Shahab Faculty of Computer System and Software Engineering, University Malaysia Pahang, Malaysia ; Electronics Department - Alhawija Technical Institute, Northern Technical University, IRAQ
Munef Abdullah Ahmed Faculty of Automatic Control and Computers, University Polytechnic of Bucharest 313 Splaiul Independentei, 060042, Romania

DOI:

https://doi.org/10.52866/ijcsm.2021.02.02.004

Keywords:

Swarm intelligence; PSO; ACO; Hybrid swarm intelligence.

Abstract

A swarm is a group of a single species in which the members interact with one another and with the
immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based
metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick,
robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling
of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation
with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm
optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends
to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive
at better quality solutions.