Applying GA-PSO-TLBO approach to engineering optimization problems

YoungSu Yun; Mitsuo Gen; Tserengotov Nomin Erdene; YoungSu Yun; Mitsuo Gen; Tserengotov Nomin Erdene

doi:10.3934/mbe.2023025

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 552-571. doi: 10.3934/mbe.2023025

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Applying GA-PSO-TLBO approach to engineering optimization problems

1.
School of Business Administration, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea
2.
Fuzzy Logic Systems Institute, Fukuoka and Tokyo University of Science, Tokyo, Japan
Academic editor: Danilo Pelusi

Received: 26 July 2022 Revised: 30 August 2022 Accepted: 20 September 2022 Published: 12 October 2022

Under addressing global competition, manufacturing companies strive to produce better and cheaper products more quickly. For a complex production system, the design problem is intrinsically a daunting optimization task often involving multiple disciplines, nonlinear mathematical model, and computation-intensive processes during manufacturing process. Here is a reason to develop a high performance algorithm for finding an optimal solution to the engineering design and/or optimization problems. In this paper, a hybrid metaheuristic approach is proposed for solving engineering optimization problems. A genetic algorithm (GA), particle swarm optimization (PSO), and teaching and learning-based optimization (TLBO), called the GA-PSO-TLBO approach, is used and demonstrated for the proposed hybrid metaheuristic approach. Since each approach has its strengths and weaknesses, the GA-PSO-TLBO approach provides an optimal strategy that maintains the strengths as well as mitigates the weaknesses, as needed. The performance of the GA-PSO-TLBO approach is compared with those of conventional approaches such as single metaheuristic approaches (GA, PSO and TLBO) and hybrid metaheuristic approaches (GA-PSO and GA-TLBO) using various types of engineering optimization problems. An additional analysis for reinforcing the performance of the GA-PSO-TLBO approach was also carried out. Experimental results proved that the GA-PSO-TLBO approach outperforms conventional competing approaches and demonstrates high flexibility and efficiency.
- hybrid metaheuristic approach,
- genetic algorithm,
- particle swarm optimization,
- teaching and learning-based optimization,
- engineering optimization problem
Citation: YoungSu Yun, Mitsuo Gen, Tserengotov Nomin Erdene. Applying GA-PSO-TLBO approach to engineering optimization problems[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 552-571. doi: 10.3934/mbe.2023025

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Abstract

Under addressing global competition, manufacturing companies strive to produce better and cheaper products more quickly. For a complex production system, the design problem is intrinsically a daunting optimization task often involving multiple disciplines, nonlinear mathematical model, and computation-intensive processes during manufacturing process. Here is a reason to develop a high performance algorithm for finding an optimal solution to the engineering design and/or optimization problems. In this paper, a hybrid metaheuristic approach is proposed for solving engineering optimization problems. A genetic algorithm (GA), particle swarm optimization (PSO), and teaching and learning-based optimization (TLBO), called the GA-PSO-TLBO approach, is used and demonstrated for the proposed hybrid metaheuristic approach. Since each approach has its strengths and weaknesses, the GA-PSO-TLBO approach provides an optimal strategy that maintains the strengths as well as mitigates the weaknesses, as needed. The performance of the GA-PSO-TLBO approach is compared with those of conventional approaches such as single metaheuristic approaches (GA, PSO and TLBO) and hybrid metaheuristic approaches (GA-PSO and GA-TLBO) using various types of engineering optimization problems. An additional analysis for reinforcing the performance of the GA-PSO-TLBO approach was also carried out. Experimental results proved that the GA-PSO-TLBO approach outperforms conventional competing approaches and demonstrates high flexibility and efficiency.

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