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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2017-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2017/6043109 |
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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 |
work_keys_str_mv |
AT nantiwatpholdee estimationofdistributionalgorithmusingcorrelationbetweenbinaryelementsanewbinarycodemetaheuristic AT sujinbureerat estimationofdistributionalgorithmusingcorrelationbetweenbinaryelementsanewbinarycodemetaheuristic |
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1725715845864226816 |