Dual-Population Social Group Optimization Algorithm Based on Human Social Group Behavior Law

Wang, Chao; Zhang, Xianqi; Niu, Yi; Gao, Shan; Jing, Jiang; Zhang, Zezhan; Yu, Peifeng; Dong, Hairong

doi:10.1109/tcss.2022.3141114

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…In the TLBO, the educational process is implemented in two phases: (a) a teaching phase in which the teacher shares knowledge with the students and (b) a learner phase in which the students share knowledge with each other [57]. Some of the other human-based optimization algorithms are the mother optimization algorithm (MOA) [58], the exchange market algorithm (EMA) [59], the group counseling optimizer (GCO) [60], the teamwork optimization algorithm (TOA) [6], dual-population social group optimization (DPSGO) [61], and the election-based optimization algorithm (EBOA) [6].…”

Section: Literature Reviewmentioning

confidence: 99%

OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems

Dehghani,

Trojovská,

Trojovský

et al. 2023

Biomimetics

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This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO’s performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications.

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