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A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy

Author

Listed:
  • Wanyi Deng

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Xiaoxue Ma

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
    Public Administration and Humanities College, Dalian Maritime University, Dalian 116026, China)

  • Weiliang Qiao

    (Marine Engineering College, Dalian Maritime University, Dalian 116026, China)

Abstract

To overcome the limitations of single-type intelligent optimization algorithms prone to becoming stuck in local optima for complex problems, a hybrid intelligent optimization algorithm named SDIQ is proposed. This algorithm combines simulated annealing (SA), differential evolution (DE), quantum-behaved particle swarm optimization (QPSO), and improved particle swarm optimization (IPSO) into a unified framework. Initially, SA is used to explore the solution space and guide individuals toward preliminary optimization. The individuals are then ranked by fitness and divided into three subpopulations, each optimized by DE, QPSO, and IPSO, respectively. After each iteration, probabilistic learning based on fitness logarithms facilitates mutual learning among subpopulations, enabling global information sharing and improvement. The experimental results demonstrate that SDIQ exhibits strong global search capability and stability in solving both standard test functions and real-world engineering problems. Compared to traditional algorithms, SDIQ enhances global convergence and solution efficiency by integrating multiple optimization strategies and leveraging inter-individual learning, providing an effective solution for complex optimization problems.

Suggested Citation

  • Wanyi Deng & Xiaoxue Ma & Weiliang Qiao, 2024. "A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy," Mathematics, MDPI, vol. 12(16), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2570-:d:1460068
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    References listed on IDEAS

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    1. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    2. Mohammad Javad Bazrkar & Soodeh Hosseini, 2023. "Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 165-186, June.
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