Impact of Chaotic Dynamics on the Performance of Metaheuristc Optimization Algorithms: an Experimental Analysis

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Impact of Chaotic Dynamics on the Performance of Metaheuristc Optimization Algorithms: an Experimental Analysis : An experimental analysis. / Zelinka, Ivan; Diep, Quoc Bao; Snášel, Václav; Das, Swagatam; Innocenti, Giacomo; Tesi, Alberto; Schoen, Fabio; Kuznetsov, Nikolay V.

In: Information Sciences, Vol. 587, 03.2022, p. 692-719.

Research output: Contribution to journal › Article › peer-review

Author

Zelinka, Ivan ; Diep, Quoc Bao ; Snášel, Václav ; Das, Swagatam ; Innocenti, Giacomo ; Tesi, Alberto ; Schoen, Fabio ; Kuznetsov, Nikolay V. / Impact of Chaotic Dynamics on the Performance of Metaheuristc Optimization Algorithms: an Experimental Analysis : An experimental analysis. In: Information Sciences. 2022 ; Vol. 587. pp. 692-719.

BibTeX

@article{723580996b5f4cc1870663aff4513ee0,

title = "Impact of Chaotic Dynamics on the Performance of Metaheuristc Optimization Algorithms: an Experimental Analysis: An experimental analysis",

abstract = "Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions.",

keywords = "Algorithm dynamics, Algorithm performance, Deterministic chaos, Evolutionary algorithms, Swarm intelligence",

author = "Ivan Zelinka and Diep, {Quoc Bao} and V{\'a}clav Sn{\'a}{\v s}el and Swagatam Das and Giacomo Innocenti and Alberto Tesi and Fabio Schoen and Kuznetsov, {Nikolay V.}",

note = "Publisher Copyright: {\textcopyright} 2021 The Author(s)",

year = "2022",

month = mar,

doi = "10.1016/j.ins.2021.10.076",

language = "English",

volume = "587",

pages = "692--719",

journal = "Information Sciences",

issn = "0020-0255",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Impact of Chaotic Dynamics on the Performance of Metaheuristc Optimization Algorithms: an Experimental Analysis

T2 - An experimental analysis

AU - Zelinka, Ivan

AU - Diep, Quoc Bao

AU - Snášel, Václav

AU - Das, Swagatam

AU - Innocenti, Giacomo

AU - Tesi, Alberto

AU - Schoen, Fabio

AU - Kuznetsov, Nikolay V.

PY - 2022/3

Y1 - 2022/3

N2 - Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions.

AB - Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions.

KW - Algorithm dynamics

KW - Algorithm performance

KW - Deterministic chaos

KW - Evolutionary algorithms

KW - Swarm intelligence

UR - http://www.scopus.com/inward/record.url?scp=85119922430&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/98842c20-b857-3593-9445-d816b438967b/

U2 - 10.1016/j.ins.2021.10.076

DO - 10.1016/j.ins.2021.10.076

M3 - Article

AN - SCOPUS:85119922430

VL - 587

SP - 692

EP - 719

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -

ID: 95231127