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Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function

by Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 23
Year of Publication: 2018
Authors: Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O
10.5120/cae2018652799

Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O . Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function. Communications on Applied Electronics. 7, 23 ( Dec 2018), 13-20. DOI=10.5120/cae2018652799

@article{ 10.5120/cae2018652799,
author = { Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O },
title = { Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function },
journal = { Communications on Applied Electronics },
issue_date = { Dec 2018 },
volume = { 7 },
number = { 23 },
month = { Dec },
year = { 2018 },
issn = { 2394-4714 },
pages = { 13-20 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number23/837-2018652799/ },
doi = { 10.5120/cae2018652799 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:02:21.902758+05:30
%A Mogaji Stephen Alaba
%A Alese Boniface Kayode
%A Adetunmbi Adebayo O
%T Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 23
%P 13-20
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swarm intelligence (SI) is the communal behavior of devolved, self-organized structures, natural or artificial. SI systems consist typically of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents This research work aims at hybridizing the conventional Particle Swarm Optimization (PSO) algorithm with the pheromone mechanism of Ant Colony Optimization (ACO) to attain faster convergence on a feasible standard PSO solution space then benchmarked against standard optimization test functions using Python Programming language to prove the correctness and convergence of the Hybridized PSO optimization mode for minimization. The result shows that hybridizing swarm intelligence performs better in solving difficult continuous optimization problems.

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Index Terms

Computer Science
Information Sciences

Keywords

Hybridization Pheromone mechanism Benchmark functions pbest gbest PSO ACO

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