Island bat algorithm for optimization

•This paper proposes island bat algorithm (iBA) for optimization problems.•iBA is evaluated using IEEE-CEC2005 functions of different types and complexity.•Experimental results suggest that iBA is not highly sensitive to its parameters.•Comparative evaluation shows the superiority of iBA over 17 com...

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Vydáno v:Expert systems with applications Ročník 107; s. 126 - 145
Hlavní autoři: Al-Betar, Mohammed Azmi, Awadallah, Mohammed A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.10.2018
Elsevier BV
Témata:
Algorithms
Bat-inspired algorithm
Diversity
Economic load dispatch
Evolutionary algorithms
Expert systems
Global optimization
Island model
Islands
Migration
Optimization
Parameter sensitivity
Searching
Sensitivity analysis
Structured population
Structured population
Global optimization
Bat-inspired algorithm
Island model
Economic load dispatch
Diversity
ISSN:0957-4174, 1873-6793
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Abstract •This paper proposes island bat algorithm (iBA) for optimization problems.•iBA is evaluated using IEEE-CEC2005 functions of different types and complexity.•Experimental results suggest that iBA is not highly sensitive to its parameters.•Comparative evaluation shows the superiority of iBA over 17 comparative methods.•iBA is utilized for Economic Load Dispatch as a real-world application. Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterations, a migration process is activated to exchange specific migrants between islands. Recently, bat-inspired algorithm has been proposed as a population-based algorithm to mimic the echolocation system involved in micro-bat. The main drawback of bat-inspired algorithm is its inability to preserve the diversity during the search and thus the prematurity can take place. In this paper, the strategy of island model is adapted for bat-inspired algorithm to empower its capability in controlling its diversity concepts. The proposed island bat-inspired algorithm is evaluated using 25 IEEE-CEC2005 benchmark functions with different size and complexity. The sensitivity analysis for the main parameters of island bat-inspired algorithm is well-studied to show their effect on the convergence properties. For comparative evaluation, island bat-inspired algorithm is compared with 17 competitive methods and shows very successful outcomes. Furthermore, the proposed algorithm is applied for three real-world cases of economic load dispatch problem where the results obtained prove considerable efficiency in comparison with other state-of-the-art methods.
AbstractList •This paper proposes island bat algorithm (iBA) for optimization problems.•iBA is evaluated using IEEE-CEC2005 functions of different types and complexity.•Experimental results suggest that iBA is not highly sensitive to its parameters.•Comparative evaluation shows the superiority of iBA over 17 comparative methods.•iBA is utilized for Economic Load Dispatch as a real-world application. Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterations, a migration process is activated to exchange specific migrants between islands. Recently, bat-inspired algorithm has been proposed as a population-based algorithm to mimic the echolocation system involved in micro-bat. The main drawback of bat-inspired algorithm is its inability to preserve the diversity during the search and thus the prematurity can take place. In this paper, the strategy of island model is adapted for bat-inspired algorithm to empower its capability in controlling its diversity concepts. The proposed island bat-inspired algorithm is evaluated using 25 IEEE-CEC2005 benchmark functions with different size and complexity. The sensitivity analysis for the main parameters of island bat-inspired algorithm is well-studied to show their effect on the convergence properties. For comparative evaluation, island bat-inspired algorithm is compared with 17 competitive methods and shows very successful outcomes. Furthermore, the proposed algorithm is applied for three real-world cases of economic load dispatch problem where the results obtained prove considerable efficiency in comparison with other state-of-the-art methods.
Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterations, a migration process is activated to exchange specific migrants between islands. Recently, bat-inspired algorithm has been proposed as a population-based algorithm to mimic the echolocation system involved in micro-bat. The main drawback of bat-inspired algorithm is its inability to preserve the diversity during the search and thus the prematurity can take place. In this paper, the strategy of island model is adapted for bat-inspired algorithm to empower its capability in controlling its diversity concepts. The proposed island bat-inspired algorithm is evaluated using 25 IEEE-CEC2005 benchmark functions with different size and complexity. The sensitivity analysis for the main parameters of island bat-inspired algorithm is well-studied to show their effect on the convergence properties. For comparative evaluation, island bat-inspired algorithm is compared with 17 competitive methods and shows very successful outcomes. Furthermore, the proposed algorithm is applied for three real-world cases of economic load dispatch problem where the results obtained prove considerable efficiency in comparison with other state-of-the-art methods.
Author Al-Betar, Mohammed Azmi
Awadallah, Mohammed A.
Author_xml – sequence: 1
  givenname: Mohammed Azmi
  orcidid: 0000-0002-1510-0130
  surname: Al-Betar
  fullname: Al-Betar, Mohammed Azmi
  email: mohbetar@bau.edu.jo
  organization: Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, 50, Al-Huson, Irbid, Jordan
– sequence: 2
  givenname: Mohammed A.
  orcidid: 0000-0002-7815-8946
  surname: Awadallah
  fullname: Awadallah, Mohammed A.
  email: ma.awadallah@alaqsa.edu.ps
  organization: Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
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Keywords Structured population
Global optimization
Bat-inspired algorithm
Island model
Economic load dispatch
Diversity
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Snippet •This paper proposes island bat algorithm (iBA) for optimization problems.•iBA is evaluated using IEEE-CEC2005 functions of different types and...
Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population...
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SubjectTerms Algorithms
Bat-inspired algorithm
Diversity
Economic load dispatch
Evolutionary algorithms
Expert systems
Global optimization
Island model
Islands
Migration
Optimization
Parameter sensitivity
Searching
Sensitivity analysis
Structured population
Title Island bat algorithm for optimization
URI https://dx.doi.org/10.1016/j.eswa.2018.04.024
https://www.proquest.com/docview/2083800928
Volume 107
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