An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin

Chen, Zhenyu; Liu, Yang; Yang, Zhile; Fu, Xuewei; Tan, Jiubin; Yang, Xiaofeng

doi:10.1016/j.swevo.2020.100766

Cited by 26 publications

(14 citation statements)

References 35 publications

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“…Teaching-learning-based optimization (TLBO) [ 31 ] simulates a class-based learning approach in which the improvement of the level of the students in the class is guided by the teacher's “teaching,” and at the same time, the students need to “learn” from each other to facilitate the absorption of knowledge. Students need to “learn” from each other to facilitate the absorption of knowledge.…”

Section: Algorithm Improvement and Validation Experimentsmentioning

confidence: 99%

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Han

Huang

et al. 2022

Computational Intelligence and Neuroscience

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To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the “cloud, fog, edge, and end” collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.

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Section: Algorithm Improvement and Validation Experimentsmentioning

confidence: 99%

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Han

Huang

et al. 2022

Computational Intelligence and Neuroscience

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“…In [57], an EO variant abbreviated as MEOA and improved by the reference point approach was proposed to address MOOPs. A novel self-adaptive hybrid self-learning TLBO (SHSLTLBO) was suggested by Chen et al [58] to overcome the origin bias of the TLBO and prevent local convergence due to initialisation. Two multi-objective variants of EO were introduced in [53] [54].…”

Section: Introductionmentioning

confidence: 99%

Solving Multi-Objective Problems Using Bird Swarm Algorithm

et al. 2021

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This paper introduces an effective method by combining the multi-objective technique with the bird swarm algorithm (BSA) to obtain a new method called MBSA. The MBSA obtains some of the different non-dominated techniques that maintain variety amongst the optimal solutions. To verify and evaluate the effectiveness of the MBSA, collections of constrained, unconstrained, and engineering problems are measured. These problems have various Pareto front (PF) properties, including non-convex, convex, and discrete PFs. The results show that the MBSA has a good ability to obtain both a better solution spread and better convergence near the true PF. Furthermore, the quantitative and qualitative results indicate that the MBSA provides high convergence and good results in all experiments and with real-world problems against well-known algorithms in the literature. INDEX TERMS Bird swarm algorithm; Multi-objective optimization; Constrained optimization problems; UnConstrained optimization problems; Engineering optimization problems; Pareto front; Pareto optimal solutions.

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“…As a result, this method could not guarantee that the obtained RBFNN model is optimal. Motivated by the above analysis, a radial basis function neural network based identification approach is proposed to obtain an accurate and continuous model of the cogging force and a self-adaptive hybrid self-learning teaching-learningbased optimization (SHSLTLBO) method [8] is utilized to train the neural network. The contribution of this paper is threefold.…”

Section: Introductionmentioning

confidence: 99%

“…The meta-heuristic optimization techniques such as the SHSLTLBO method belong to the multidimension zero-order nonlinear optimization operator, which provides an effective optimization tool for the complicated practical off-line nonlinear problems [9]. Furthermore, based on the hybrid self-adaptive mutation frame, the SHSLTLBO makes better tradeoff between the exploration and the exploitation capacity of teaching-learning-based optimization (TLBO) variants [8]. Finally, an experiment on the linear motor demonstrates the effectiveness and the superiority of the proposed cogging force identification method.…”

Section: Introductionmentioning

confidence: 99%

Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors

Ding

Zanchetta

et al. 2021

2021 13th International Symposium on Linear Drives for Industry Applications (LDIA)

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The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method.

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An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin

Cited by 26 publications

References 35 publications

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Solving Multi-Objective Problems Using Bird Swarm Algorithm

Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors

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