Artigo Acesso aberto Revisado por pares

Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

2022; Institute of Electrical and Electronics Engineers; Volume: 10; Linguagem: Inglês

10.1109/access.2022.3227510

ISSN

2169-3536

Autores

Florentina Yuni Arini, Khamron Sunat, Chitsutha Soomlek,

Tópico(s)

Artificial Intelligence in Games

Resumo

This paper presents the logical relationships of Aristotle's square of opposition on four basic categorial prepositions (i.e., contrary, contradictory, subcontrary, and subaltern) of Joint Opposite Selection (JOS). JOS brings a mutual reinforcement by joining two opposition strategies of Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. We also propose an enhancement of Golden Jackal Optimization (GJO) with Joint Opposite Selection named GJO-JOS. In the optimization process, JOS assists GJO in assaulting the prey swiftly using SLO. DO assists GJO in finding better chances to locate the fittest prey. With JOS, the GJO succeeds in elevating its performance. We evaluate the performance of GJO-JOS in a competition of CEC 2017 on a set of 29 benchmark functions. The benchmark functions include unimodal, multimodal, hybrid, and composition. Based on these benchmark functions, the evaluation results of GJO-JOS are competitive to GJO with seven single opposition-based learning strategies (OBLs). We also compare GJO-JOS to eight nature-inspired algorithms including the original version of GJO. GJO-JOS produces promising results among seven single OBLs, eight natured-inspired algorithms, and GJO. The experimental results confirm that GJO-JOS generates equilibrium in the balance mechanism effectively.

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