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Neural Computing and Applications

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03-06-2022 | Original Article

A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems

Authors: Changting Zhong, Gang Li, Zeng Meng

Published in: Neural Computing and Applications | Issue 19/2022

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Abstract

Slime mould algorithm (SMA) is a novel metaheuristic algorithm with good performance for optimization problems, but it may encounter premature or low accuracy in complex optimization problems. This paper presents a hybrid SMA using teaching–learning based optimization (TLBO), called TLSMA, for solving global optimization and reliability-based design optimization (RBDO) problems. The key point of TLSMA is to combine the capacities of exploration and exploitation from SMA and TLBO, which can enhance the convergence ability of SMA. Moreover, the TLSMA is extended for solving RBDO problems under probabilistic constraints, which are handled by the adaptive chaos control method. The proposed algorithm is tested by a series of experiments with two parts. First, TLSMA is verified by 24 well-known benchmark optimization problems with unimodal and multimodal functions, and is compared with several state-of-the-art metaheuristic algorithms. The results of benchmark optimization problems show that TLSMA outperforms PSO, BBO, GWO, WOA, SSA, TLBO and SMA. Then, TLSMA-RBDO is tested by five RBDO problems, including a numerical and four engineering problems. The results illustrate that the proposed algorithm has high performance in the RBDO problems, which is significantly superior to the compared algorithms.

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Title: A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems
Authors: Changting Zhong
Gang Li
Zeng Meng
Publication date: 03-06-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 19/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07277-3

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Research on the human heat transfer model of Chinese pilots and experimental verification of model correctness

Modeling the compressive strength of concrete containing waste glass using multi-objective automatic regression

A teacher-student framework for liver and tumor segmentation under mixed supervision from abdominal CT scans

Automatic scene generation using sentiment analysis and bidirectional recurrent neural network with multi-head attention

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