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Higher-Order Thinking Skills Optimizer: A Metaheuristic Algorithm Inspired by Human Behavior and Its Application in

Zhixin Han1, Ying Qiao1, Hongxin Fu1

  • 1Ningxia Collaborative Innovation Center for Scientific Computing and Intelligent Information Processing, School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.

Biomimetics (Basel, Switzerland)
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

A new meta-heuristic algorithm, the Higher-order Thinking Skills Optimizer (HTSO), effectively solves complex optimization problems. HTSO demonstrates superior performance and robustness in engineering and path planning tasks.

Keywords:
constrained problemsengineering designhigher-order thinking skillshuman-inspired algorithmmetaheuristicoptimization algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Artificial Intelligence

Background:

  • Increasing complexity of optimization problems necessitates advanced computational methods.
  • Existing meta-heuristic algorithms offer flexibility but require further enhancement for complex, high-dimensional challenges.
  • Higher-Order Thinking Skills (HOTS) from educational theory provide a novel framework for developing robust optimization strategies.

Purpose of the Study:

  • Introduce the Higher-order Thinking Skills Optimizer (HTSO), a novel meta-heuristic algorithm.
  • Evaluate HTSO's performance and robustness on benchmark functions and real-world problems.
  • Demonstrate HTSO's effectiveness in complex engineering and path planning applications.

Main Methods:

  • HTSO simulates four aspects of HOTS: creativity (exploration), problem-solving (exploitation), critical thinking (metacognitive control), and decision-making.
  • Creativity and problem-solving drive exploration and exploitation, while critical thinking balances them by evaluating solution quality.
  • The algorithm is designed for user-friendliness with minimal parameter tuning and demonstrated robustness.

Main Results:

  • HTSO significantly outperformed 21 other algorithms, including CEC champions, on CEC benchmark sets across various dimensions.
  • In real-world constrained engineering optimization, HTSO surpassed 14 comparative algorithms.
  • HTSO achieved the best performance in 3D path planning for unmanned aerial vehicles in complex mountainous scenarios.

Conclusions:

  • HTSO is a highly effective and robust meta-heuristic algorithm for diverse and complex optimization challenges.
  • The HOTS-inspired approach offers a promising direction for developing advanced optimization techniques.
  • HTSO demonstrates significant potential for practical applications in engineering and autonomous systems.