Improved artificial fish swarm algorithm based on social learning mechanism

Zheng, Yu; Liu, Jingjing; Wang, Ning

doi:10.3724/sp.j.1087.2013.01305

Cited by 4 publications

(2 citation statements)

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“…Similarly, Shukla et al (2018) improved TLBO in the teaching phase; each learner's knowledge update comes from the teacher and his/her own old knowledge. Tong et al (2018) improved TLBO as the self-learning mechanism-based TLBO (SLTLBO) algorithm; in this teaching phase, learners improve their knowledge by learning from the difference between the teacher and the worst learner. Zou et al (2018) also improved the TLBO by introducing differential evolution (DE) operator into the teaching phase to increase the diversity of the new population.…”

Section: Introductionmentioning

confidence: 99%

A novel improved teaching and learning-based-optimization algorithm and its application in a large-scale inventory control system

Chen

2023

IJICC

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PurposeThe purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.Design/methodology/approachUtilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.FindingsExperiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.Originality/valueThe innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

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Section: Introductionmentioning

confidence: 99%

A novel improved teaching and learning-based-optimization algorithm and its application in a large-scale inventory control system

Chen

2023

IJICC

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“…The basic fish swarm algorithm mainly uses the forage of the fish, bunching and tailgating to finish optimizing procedure. The bunching makes the artificial fish trapped into the local extremum to gather toward the artificial fish trend to global extremum, so that it can escape local extremum; the tailgating makes the artificial fish trapped into the local extremum to followed in the wake of the global optimal artificial fish to escape local extremum [8].…”

Section: Artificial Fish Swarm Algorithmmentioning

confidence: 99%