An improved Jaya optimization algorithm with Lévy flight

Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named “Lévy flight”) with occasional “big” steps. This characteristic makes Lévy flight especially useful for performing large “jumps” that allow the search to escape from...

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Vydané v:	Expert systems with applications Ročník 165; s. 113902
Hlavní autori:	Iacca, Giovanni, dos Santos Junior, Vlademir Celso, Veloso de Melo, Vinícius
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 01.03.2021 Elsevier BV
Predmet:	Algorithms Benchmarks Continuous optimization Jaya Levy distribution Lévy flight Optimization Optimization algorithms Random walk Swarm intelligence Swarm intelligence Jaya Continuous optimization Lévy flight
ISSN:	0957-4174, 1873-6793
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Abstract	Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named “Lévy flight”) with occasional “big” steps. This characteristic makes Lévy flight especially useful for performing large “jumps” that allow the search to escape from a local optimum and restart in a different region of the search space. In this paper, we investigate this idea by applying Lévy flight to Jaya, a simple yet effective Swarm Intelligence optimization algorithm recently proposed in the literature. We perform experiments on the CEC 2014 benchmark as well as five industrial optimization problems taken from the CEC 2011 benchmark, and compare the performance of the proposed Lévy flight Jaya Algorithm (LJA) against several state-of-the-art algorithms for continuous optimization. Our numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems. •We present the Lévy flight Jaya Algorithm (LJA) for continuous optimization.•We evaluate the effect of the β parameter on LJA.•LJA consistently outperforms the original Jaya on most optimization problems.•We compare LJA with state-of-the-art algorithms on benchmark/industrial problems.
AbstractList	Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named "Lévy flight") with occasional "big" steps. This characteristic makes Lévy flight especially useful for performing large "jumps" that allow the search to escape from a local optimum and restart in a different region of the search space. In this paper, we investigate this idea by applying Lévy flight to Jaya, a simple yet effective Swarm Intelligence optimization algorithm recently proposed in the literature. We perform experiments on the CEC 2014 benchmark as well as five industrial optimization problems taken from the CEC 2011 benchmark, and compare the performance of the proposed Lévy flight Jaya Algorithm (LJA) against several state-of-the-art algorithms for continuous optimization. Our numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems. Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named “Lévy flight”) with occasional “big” steps. This characteristic makes Lévy flight especially useful for performing large “jumps” that allow the search to escape from a local optimum and restart in a different region of the search space. In this paper, we investigate this idea by applying Lévy flight to Jaya, a simple yet effective Swarm Intelligence optimization algorithm recently proposed in the literature. We perform experiments on the CEC 2014 benchmark as well as five industrial optimization problems taken from the CEC 2011 benchmark, and compare the performance of the proposed Lévy flight Jaya Algorithm (LJA) against several state-of-the-art algorithms for continuous optimization. Our numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems. •We present the Lévy flight Jaya Algorithm (LJA) for continuous optimization.•We evaluate the effect of the β parameter on LJA.•LJA consistently outperforms the original Jaya on most optimization problems.•We compare LJA with state-of-the-art algorithms on benchmark/industrial problems.
ArticleNumber	113902
Author	Iacca, Giovanni Veloso de Melo, Vinícius dos Santos Junior, Vlademir Celso
Author_xml	– sequence: 1 givenname: Giovanni surname: Iacca fullname: Iacca, Giovanni email: giovanni.iacca@unitn.it organization: Department of Information Engineering and Computer Science, University of Trento, Povo, Italy – sequence: 2 givenname: Vlademir Celso surname: dos Santos Junior fullname: dos Santos Junior, Vlademir Celso email: vcsjunior@unifesp.br organization: Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, São Paulo, Brazil – sequence: 3 givenname: Vinícius surname: Veloso de Melo fullname: Veloso de Melo, Vinícius email: vvdemelo@wawanesa.com organization: The Wawanesa Mutual Insurance Company, Data Analytics, Winnipeg, Manitoba, Canada
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Snippet	Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named “Lévy flight”) with... Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named "Lévy flight") with...
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SubjectTerms	Algorithms Benchmarks Continuous optimization Jaya Levy distribution Lévy flight Optimization Optimization algorithms Random walk Swarm intelligence
Title	An improved Jaya optimization algorithm with Lévy flight
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