Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287957 
Year of Publication: 
2022
Citation: 
[Journal:] International Transactions in Operational Research [ISSN:] 1475-3995 [Volume:] 30 [Issue:] 6 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] 3361-3397
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.
Subjects: 
metaheuristics
initialization
evolutionary algorithms
swarm intelligence
local search
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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