Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems

Karam M. Sallam; Saber M. Elsayed; Ruhul A. Sarker; Daryl L. Essam

doi:10.1109/CEC.2017.7969461

Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems

Karam M. Sallam, Saber M. Elsayed, Ruhul A. Sarker, Daryl L. Essam

Research output: Contribution to conference (non-published works) › Paper › peer-review

42 Citations (Scopus)

Abstract

Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.

Original language	English
Pages	1350-1357
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2017.7969461
Publication status	Published - 5 Jul 2017
Externally published	Yes
Event	2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain Duration: 5 Jun 2017 → 8 Jun 2017

Conference

Conference	2017 IEEE Congress on Evolutionary Computation, CEC 2017
Country/Territory	Spain
City	Donostia-San Sebastian
Period	5/06/17 → 8/06/17

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10.1109/CEC.2017.7969461

Cite this

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title = "Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems",

abstract = "Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.",

keywords = "Differential evolution, Multi-method, Multi-operator, Orthogonal experimental design",

author = "Sallam, {Karam M.} and Elsayed, {Saber M.} and Sarker, {Ruhul A.} and Essam, {Daryl L.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Congress on Evolutionary Computation, CEC 2017 ; Conference date: 05-06-2017 Through 08-06-2017",

year = "2017",

month = jul,

day = "5",

doi = "10.1109/CEC.2017.7969461",

language = "English",

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Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems. / Sallam, Karam M.; Elsayed, Saber M.; Sarker, Ruhul A. et al.
2017. 1350-1357 Paper presented at 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia-San Sebastian, Spain.

Research output: Contribution to conference (non-published works) › Paper › peer-review

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T1 - Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems

AU - Sallam, Karam M.

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AU - Sarker, Ruhul A.

AU - Essam, Daryl L.

PY - 2017/7/5

Y1 - 2017/7/5

N2 - Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.

AB - Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.

KW - Differential evolution

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KW - Multi-operator

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T2 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017

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Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems

Abstract

Conference

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