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Applying GA-PSO-TLBO approach to engineering optimization problems.

YoungSu Yun1, Mitsuo Gen2, Tserengotov Nomin Erdene1

  • 1School of Business Administration, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea.

Mathematical Biosciences and Engineering : MBE
|January 18, 2023
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Summary
This summary is machine-generated.

A new hybrid metaheuristic approach, GA-PSO-TLBO, combines Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Teaching-Learning-Based Optimization (TLBO) to solve complex engineering design problems efficiently.

Keywords:
engineering optimization problemgenetic algorithmhybrid metaheuristic approachparticle swarm optimizationteaching and learning-based optimization

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Area of Science:

  • Engineering Optimization
  • Computational Intelligence
  • Metaheuristic Algorithms

Background:

  • Manufacturing companies face global competition, requiring faster, cheaper, and better product development.
  • Complex production systems present daunting optimization challenges due to multidisciplinary factors, nonlinear models, and intensive computations.
  • High-performance algorithms are crucial for optimal solutions in engineering design and manufacturing.

Purpose of the Study:

  • To propose and demonstrate a novel hybrid metaheuristic approach for solving engineering optimization problems.
  • To leverage the strengths and mitigate the weaknesses of individual metaheuristic algorithms through hybridization.
  • To evaluate the performance of the proposed approach against conventional methods.

Main Methods:

  • A hybrid metaheuristic approach named GA-PSO-TLBO was developed, integrating Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning-Based Optimization (TLBO).
  • The performance of GA-PSO-TLBO was benchmarked against single metaheuristic approaches (GA, PSO, TLBO) and other hybrid approaches (GA-PSO, GA-TLBO).
  • Various engineering optimization problems were utilized to assess the effectiveness and flexibility of the proposed algorithm.

Main Results:

  • The GA-PSO-TLBO approach demonstrated superior performance compared to conventional single and hybrid metaheuristic methods.
  • Experimental results confirmed the high flexibility and efficiency of the proposed GA-PSO-TLBO approach.
  • Additional analyses reinforced the enhanced performance achieved by the GA-PSO-TLBO strategy.

Conclusions:

  • The GA-PSO-TLBO hybrid metaheuristic approach is an effective and efficient strategy for tackling complex engineering optimization problems.
  • This novel approach offers a robust solution for industries seeking to improve product design and manufacturing processes.
  • The findings highlight the potential of combining different metaheuristics to overcome individual limitations and achieve better optimization outcomes.