Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization

Particle Swarm Optimization (PSO) and Gravitational Search Algorithm are a well known population-based heuristic optimization techniques. PSO is inspired from a motion flock of birds searching for a food. In PSO, a bird adjusts its position according to its own ‘‘experience’’ as well as the experien...

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Main Authors:	Zuwairie, Ibrahim, Mohamad Nizam, Aliman, Fardila, Naim, Sophan Wahyudi, Nawawi, Shahdan, Sudin
Format:	Article
Language:	English
Published:	UTM Press 2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/9269/ http://umpir.ump.edu.my/id/eprint/9269/ http://umpir.ump.edu.my/id/eprint/9269/1/Performance%20evaluation%20of%20Black%20Hole%20Algorithm%2C%20Gravitational%20Search%20Algorithm%20and%20Particle%20Swarm%20Optimization.pdf

id	ump-9269
recordtype	eprints
spelling	ump-92692018-02-08T03:26:56Z http://umpir.ump.edu.my/id/eprint/9269/ Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization Zuwairie, Ibrahim Mohamad Nizam, Aliman Fardila, Naim Sophan Wahyudi, Nawawi Shahdan, Sudin TK Electrical engineering. Electronics Nuclear engineering Particle Swarm Optimization (PSO) and Gravitational Search Algorithm are a well known population-based heuristic optimization techniques. PSO is inspired from a motion flock of birds searching for a food. In PSO, a bird adjusts its position according to its own ‘‘experience’’ as well as the experience of other birds. Tracking and memorizing the best position encountered build bird’s experience which will leads to optimal solution. GSA is based on the Newtonian gravity and motion laws between several masses. In GSA, the heaviest mass presents an optimum solution in the search space. Other agents inside the population are attracted to heaviest mass and will finally converge to produce best solution. Black Hole Algorithm (BH) is one of the optimization technique recently proposed for data clustering problem. BH algorithm is inspired by the natural universe phenomenon called "black hole”. In BH algorithm, the best solution is selected to be the black hole and the rest of candidates which are called stars will be drawn towards the black hole. In this paper, performance of BH algorithm will be analyzed and reviewed for continuous search space using CEC2014 benchmark dataset against Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). CEC2014 benchmark dataset contains 4 unimodal, 7 multimodal and 6 hybrid functions. Several common parameters has been chosen to make an equal comparison between these algorithm such as size of population is 30, 1000 iteration, 30 dimension and 30 times of experiment. UTM Press 2015 Article PeerReviewed application/pdf en cc_by_nc http://umpir.ump.edu.my/id/eprint/9269/1/Performance%20evaluation%20of%20Black%20Hole%20Algorithm%2C%20Gravitational%20Search%20Algorithm%20and%20Particle%20Swarm%20Optimization.pdf Zuwairie, Ibrahim and Mohamad Nizam, Aliman and Fardila, Naim and Sophan Wahyudi, Nawawi and Shahdan, Sudin (2015) Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization. Malaysian Journal of Fundamental and Applied Sciences, 11 (1). pp. 10-20. ISSN 2289-5981 http://www.mjfas.utm.my/index.php/mjfas/article/view/342
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Zuwairie, Ibrahim Mohamad Nizam, Aliman Fardila, Naim Sophan Wahyudi, Nawawi Shahdan, Sudin Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization
description	Particle Swarm Optimization (PSO) and Gravitational Search Algorithm are a well known population-based heuristic optimization techniques. PSO is inspired from a motion flock of birds searching for a food. In PSO, a bird adjusts its position according to its own ‘‘experience’’ as well as the experience of other birds. Tracking and memorizing the best position encountered build bird’s experience which will leads to optimal solution. GSA is based on the Newtonian gravity and motion laws between several masses. In GSA, the heaviest mass presents an optimum solution in the search space. Other agents inside the population are attracted to heaviest mass and will finally converge to produce best solution. Black Hole Algorithm (BH) is one of the optimization technique recently proposed for data clustering problem. BH algorithm is inspired by the natural universe phenomenon called "black hole”. In BH algorithm, the best solution is selected to be the black hole and the rest of candidates which are called stars will be drawn towards the black hole. In this paper, performance of BH algorithm will be analyzed and reviewed for continuous search space using CEC2014 benchmark dataset against Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). CEC2014 benchmark dataset contains 4 unimodal, 7 multimodal and 6 hybrid functions. Several common parameters has been chosen to make an equal comparison between these algorithm such as size of population is 30, 1000 iteration, 30 dimension and 30 times of experiment.
format	Article
author	Zuwairie, Ibrahim Mohamad Nizam, Aliman Fardila, Naim Sophan Wahyudi, Nawawi Shahdan, Sudin
author_facet	Zuwairie, Ibrahim Mohamad Nizam, Aliman Fardila, Naim Sophan Wahyudi, Nawawi Shahdan, Sudin
author_sort	Zuwairie, Ibrahim
title	Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization
title_short	Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization
title_full	Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization
title_fullStr	Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization
title_full_unstemmed	Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization
title_sort	performance evaluation of black hole algorithm, gravitational search algorithm and particle swarm optimization
publisher	UTM Press
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/9269/ http://umpir.ump.edu.my/id/eprint/9269/ http://umpir.ump.edu.my/id/eprint/9269/1/Performance%20evaluation%20of%20Black%20Hole%20Algorithm%2C%20Gravitational%20Search%20Algorithm%20and%20Particle%20Swarm%20Optimization.pdf
first_indexed	2023-09-18T22:07:40Z
last_indexed	2023-09-18T22:07:40Z
_version_	1777414816875413504

Performance evaluation of Black Hole Algorithm, Gravitational Search Algorithm and Particle Swarm Optimization

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