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HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex

Shouheng Tuo1, Longquan Yong1, Fang'an Deng1

  • 1School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China.

Plos One
|April 14, 2017
PubMed
Summary
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This study introduces HSTLBO, a hybrid algorithm combining Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO). HSTLBO synergistically solves complex optimization problems, enhancing both exploration and exploitation for superior performance.

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) are effective swarm intelligence algorithms for NP-Hard problems.
  • Both HS and TLBO exhibit limitations in complex high-dimensional problems, with HS showing strong exploration but slow convergence, and TLBO fast convergence but susceptibility to local optima.
  • HS and TLBO demonstrate complementary strengths, suggesting potential for synergistic integration.

Purpose of the Study:

  • To develop a hybrid optimization algorithm, HSTLBO, by merging HS and TLBO with a self-adaptive selection strategy.
  • To enhance the balance between global exploration and local exploitation for complex optimization tasks.
  • To evaluate the performance of HSTLBO against state-of-the-art HS and TLBO variants and assess its applicability to real-world problems.

Related Experiment Videos

Main Methods:

  • A novel hybrid algorithm, HSTLBO, was developed by integrating modified HS and TLBO components.
  • A self-adaptive selection strategy was employed to dynamically balance the contributions of HS and TLBO.
  • HS was primarily utilized for global exploration, while TLBO focused on rapid local exploitation.

Main Results:

  • HSTLBO demonstrated superior performance and speed compared to five state-of-the-art HS variants.
  • HSTLBO exhibited enhanced exploration capabilities compared to five well-regarded TLBO variants, maintaining similar run times.
  • Experimental results on portfolio optimization problems confirmed the effectiveness of HSTLBO for complex real-world applications.

Conclusions:

  • The proposed HSTLBO algorithm effectively synergizes HS and TLBO, overcoming individual limitations.
  • HSTLBO offers a promising approach for solving complex high-dimensional optimization problems.
  • The hybrid strategy proves effective in balancing exploration and exploitation, leading to robust performance in practical scenarios.