A comprehensive review on recent intelligent metaheuristic algorithms. (20th May 2022)

• Record Type: Journal Article
• Authors: Rajalakshmi, S.; Kanmani, S.
• Is Part Of: International journal of swarm intelligence. Volume 7:Number 2(2022)
• Page Start: 182
• Page End: 205
• Publication Date: 2022-05-20
• Subjects: metaheuristics -- optimisation -- swarm intelligence -- improved metaheuristics; Swarm intelligence -- Periodicals; 006.3
• Languages: English
• ISSNs: 2049-4041

Abstract

Metaheuristics is an interesting research area with significant advances in solving problems with optimisation. Substantial advancements in metaheuristic are being made, and various new algorithms are being developed every day. The analyses in this area will undoubtedly be helpful for future improvements. This paper's main objective is to conduct a literature review of some recent algorithms motivated by nature to compare their features. This paper reviews some recently published nature inspired algorithms such as squirrel search algorithm (SSA), improved squirrel search algorithm (ISSA), grey wolf optimiser (GWO) algorithm, random walk grey wolf optimiser (RW_GWO) algorithm, sailfish optimiser (SAO) algorithm, sandpiper optimisation algorithm (SOA), search and rescue operations (SRO) algorithm, slime mould optimisation (SMO) algorithm, grasshopper optimisation algorithm (GOA) and opposition based learning grasshopper optimisation algorithm (OBLGOA). This paper focuses on a brief introduction of these algorithms and key concepts involved in formulation of swarm intelligence. Finally, this work outlines the directions for conducting effective future research.