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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

Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O

Mogaji Stephen Alaba, Alese Boniface Kayode and 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):13-20, December 2018. BibTeX

	author = {Mogaji Stephen Alaba and Alese Boniface Kayode and 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 = {December 2018},
	volume = {7},
	number = {23},
	month = {Dec},
	year = {2018},
	issn = {2394-4714},
	pages = {13-20},
	numpages = {8},
	url = {},
	doi = {10.5120/cae2018652799},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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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Hybridization, Pheromone mechanism, Benchmark functions, pbest, gbest, PSO, ACO