Accelerated Cuckoo Search With Extended Diversification and Intensification

Deepak Garg; Pardeep Kumar

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Accelerated Cuckoo Search With Extended Diversification and Intensification

机译：Accelerated Cuckoo Search With Extended Diversification and Intensification

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Metaheuristics have been great to solve NP-hard class problems in the deterministic time, but due to so many parameter settings, they lack in generality (i.e., not easy to implement on all types of problems) and also lack in global search. But the cuckoo search (CS) algorithm has only one parameter as input and also has a good reachable probability to global solution due to Levy flight. But this algorithm lacks self-adaptive parameters and extended strategies. In this paper, a deep study and improvement of cuckoo search performance has been done by introducing self-adaptive step size, extended alien egg discovery replacement (on each dimension with the use of good neighbor study), and adaptive discovery probability, and it has been named accelerated cuckoo search (ACS). Then this ACS has been utilized as an example in the load balancing problem in cloud with minimum makespan time as an objective parameter to evaluate the performance of ACS over CS. Furthermore, to validate ACS superiority over CS in all problems, these have been successfully compared on a few benchmark functions.

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来源
《International journal of swarm intelligence research》 |2021年第3期|125-148|共24页
作者
Deepak Garg; Pardeep Kumar;
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作者单位

National Institute of Technology, Kurukshetra;

Kurukshetra University;

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原文格式 PDF
正文语种英语
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关键词
Cloud Computing; Cuckoo Search; Discovery Probability; Levy Flight; Load Balancing; Random Walk; Step Size;

Accelerated Cuckoo Search With Extended Diversification and Intensification

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