Estimation-based Metaheuristics: A New Branch of Computational Intelligence

In this paper, a new branch of computational intelligence named estimation-based metaheuristic is introduced. Metaheuristic algorithms can be classified based on their source of inspiration. Besides biology, physics and chemistry, state estimation algorithm also has become a source of inspiration fo...

Full description

Bibliographic Details
Main Authors:	Nor Hidayati, Abd Aziz, Zuwairie, Ibrahim, Saifudin, Razali, Nor Azlina, Ab. Aziz
Format:	Conference or Workshop Item
Language:	English
Published:	2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/14583/ http://umpir.ump.edu.my/id/eprint/14583/1/P064%20pg469-476.pdf

id	ump-14583
recordtype	eprints
spelling	ump-145832018-02-08T02:47:52Z http://umpir.ump.edu.my/id/eprint/14583/ Estimation-based Metaheuristics: A New Branch of Computational Intelligence Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz TK Electrical engineering. Electronics Nuclear engineering In this paper, a new branch of computational intelligence named estimation-based metaheuristic is introduced. Metaheuristic algorithms can be classified based on their source of inspiration. Besides biology, physics and chemistry, state estimation algorithm also has become a source of inspiration for developing metaheuristic algorithms. Inspired by the estimation capability of Kalman Filter, Simulated Kalman Filter, SKF, uses a population of agents to make estimations of the optimum. Each agent in SKF acts as a Kalman Filter. By adapting the standard Kalman Filter framework, each individual agent finds an optimization solution by using a simulated measurement process that is guided by a best-so-far solution as a reference. Heuristic Kalman Algorithm (HKA) also is inspired by the Kalman Filter framework. HKA however, explicitly consider the optimization problem as a measurement process in generating the estimate of the optimum. In evaluating the performance of the estimation-based algorithms, it is implemented to 30 benchmark functions of the CEC 2014 benchmark suite. Statistical analysis is then carried out to rank the estimation-based algorithms’ results to those obtained by other metaheuristic algorithms. The experimental results show that the estimation-based metaheuristic is a promising approach to solving global optimization problem and demonstrates a competitive performance to some well-known metaheuristic algorithms 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14583/1/P064%20pg469-476.pdf Nor Hidayati, Abd Aziz and Zuwairie, Ibrahim and Saifudin, Razali and Nor Azlina, Ab. Aziz (2016) Estimation-based Metaheuristics: A New Branch of Computational Intelligence. In: Proceedings of The National Conference for Postgraduate Research (NCON-PGR 2016), 24-25 September 2016 , Universiti Malaysia Pahang (UMP), Pekan, Pahang. pp. 469-476..
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 Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz Estimation-based Metaheuristics: A New Branch of Computational Intelligence
description	In this paper, a new branch of computational intelligence named estimation-based metaheuristic is introduced. Metaheuristic algorithms can be classified based on their source of inspiration. Besides biology, physics and chemistry, state estimation algorithm also has become a source of inspiration for developing metaheuristic algorithms. Inspired by the estimation capability of Kalman Filter, Simulated Kalman Filter, SKF, uses a population of agents to make estimations of the optimum. Each agent in SKF acts as a Kalman Filter. By adapting the standard Kalman Filter framework, each individual agent finds an optimization solution by using a simulated measurement process that is guided by a best-so-far solution as a reference. Heuristic Kalman Algorithm (HKA) also is inspired by the Kalman Filter framework. HKA however, explicitly consider the optimization problem as a measurement process in generating the estimate of the optimum. In evaluating the performance of the estimation-based algorithms, it is implemented to 30 benchmark functions of the CEC 2014 benchmark suite. Statistical analysis is then carried out to rank the estimation-based algorithms’ results to those obtained by other metaheuristic algorithms. The experimental results show that the estimation-based metaheuristic is a promising approach to solving global optimization problem and demonstrates a competitive performance to some well-known metaheuristic algorithms
format	Conference or Workshop Item
author	Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz
author_facet	Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Nor Azlina, Ab. Aziz
author_sort	Nor Hidayati, Abd Aziz
title	Estimation-based Metaheuristics: A New Branch of Computational Intelligence
title_short	Estimation-based Metaheuristics: A New Branch of Computational Intelligence
title_full	Estimation-based Metaheuristics: A New Branch of Computational Intelligence
title_fullStr	Estimation-based Metaheuristics: A New Branch of Computational Intelligence
title_full_unstemmed	Estimation-based Metaheuristics: A New Branch of Computational Intelligence
title_sort	estimation-based metaheuristics: a new branch of computational intelligence
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/14583/ http://umpir.ump.edu.my/id/eprint/14583/1/P064%20pg469-476.pdf
first_indexed	2023-09-18T22:18:30Z
last_indexed	2023-09-18T22:18:30Z
_version_	1777415498829398016

Estimation-based Metaheuristics: A New Branch of Computational Intelligence

Similar Items