Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm

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DC Field	Value	Language
dc.contributor.author	Bassel, Atheer	-
dc.date.accessioned	2022-10-19T22:59:44Z	-
dc.date.available	2022-10-19T22:59:44Z	-
dc.date.issued	2016	-
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/3617	-
dc.description.abstract	The Glowworm Swarm Optimization (GSO) is a population-based metaheuristic algorithm for optimization problem. Disadvantages of GSO are low accuracy, convergence speed and weakness in the capability of global search which need to be improved. Thus, Memory Mechanism and Mutation Glowworm Swarm Optimization (MMGSO) are proposed in this study to find a solution for this problem. The proposed method is examined on Unimodal and Multimodal benchmark functions to enhance the GSO algorithm of solution quality, convergence speed and robustness. The results of MMGSO are analyzed and compared with GSO to prove the efficiency of the proposed method	en_US
dc.language.iso	en	en_US
dc.publisher	2017 IEEE 7th Annual Computing and Communication	en_US
dc.subject	Glowworm Swarm Optimization	en_US
dc.subject	Mutation; Memory less;	en_US
dc.title	Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm	en_US
dc.type	Article	en_US
Appears in Collections:	مركز الحاسبة الالكترونية

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