Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represe...

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Main Authors: Nantiwat Pholdee, Sujin Bureerat
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2017/6043109
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spelling doaj-dbe4da265d1f4afc969d98269f5ff7db2020-11-24T22:37:43ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472017-01-01201710.1155/2017/60431096043109Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code MetaheuristicNantiwat Pholdee0Sujin Bureerat1Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandSustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandA new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.http://dx.doi.org/10.1155/2017/6043109
collection DOAJ
language English
format Article
sources DOAJ
author Nantiwat Pholdee
Sujin Bureerat
spellingShingle Nantiwat Pholdee
Sujin Bureerat
Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic
Mathematical Problems in Engineering
author_facet Nantiwat Pholdee
Sujin Bureerat
author_sort Nantiwat Pholdee
title Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic
title_short Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic
title_full Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic
title_fullStr Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic
title_full_unstemmed Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic
title_sort estimation of distribution algorithm using correlation between binary elements: a new binary-code metaheuristic
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2017-01-01
description A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.
url http://dx.doi.org/10.1155/2017/6043109
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