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CAM-ADX: A New Genetic Algorithm with Increased Intensification and Diversification for Design Optimization Problems with Real Variables

Published online by Cambridge University Press:  01 March 2019

Edson Koiti Kudo Yasojima Open the ORCID record for Edson Koiti Kudo Yasojima [Opens in a new window] ,
Roberto Célio Limão de Oliveira ,
Otávio Noura Teixeira  and
Rodrigo Lisbôa Pereira
Edson Koiti Kudo Yasojima*
Affiliation:
Faculty of Computer Engineering, Federal University of Pará, Rua Augusto Corrêa 01, Caixa Postal 479, Guamá, Belém, Pará CEP:66075-100, Brazil E-mails: limao@ufpa.br; rod.lisboa@gmail.com
Roberto Célio Limão de Oliveira
Affiliation:
Faculty of Computer Engineering, Federal University of Pará, Rua Augusto Corrêa 01, Caixa Postal 479, Guamá, Belém, Pará CEP:66075-100, Brazil E-mails: limao@ufpa.br; rod.lisboa@gmail.com
Otávio Noura Teixeira
Affiliation:
Faculty of Computer Engineering, Federal University of Pará, Campus Universitário de Tucuruí, Rua Itaipu, 36 - Vila Permanente, Tucuruí, Pará CEP:68464-000, Brazil E-mail: onoura@gmail.com
Rodrigo Lisbôa Pereira
Affiliation:
Faculty of Computer Engineering, Federal University of Pará, Campus Universitário de Tucuruí, Rua Itaipu, 36 - Vila Permanente, Tucuruí, Pará CEP:68464-000, Brazil E-mail: onoura@gmail.com
*
*Corresponding author. E-mail: koitiyasojima@gmail.com
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Summary

This paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.

Keywords

Genetic algorithmDesign optimizationReal variablesEngineering design

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Articles
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Robotica , Volume 37 , Issue 9 , September 2019 , pp. 1595 - 1640
Copyright
© Cambridge University Press 2019 

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