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Dynamic-opposite learning enhanced meta-heuristic approach for solving multiple industrial optimization problems.

Hao Wu1,2,3, Lijuan Li4,5, Guohui Wang3

  • 1School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, 130022, Jilin, China.

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|April 20, 2026
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Summary
This summary is machine-generated.

We introduce Dynamic-Opposite Learning Differential Evolution Moth-Flame Optimization (DOLDEMFO), an enhanced algorithm for complex industrial optimization problems like robot trajectory planning. DOLDEMFO improves search efficiency and performance across various tasks.

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Area of Science:

  • Industrial Engineering
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Advanced industrial manufacturing faces NP-hard optimization problems, including robot trajectory planning and job shop scheduling.
  • Existing optimization methods often struggle with the complexity and integrated nature of these industrial challenges.

Purpose of the Study:

  • To develop an enhanced metaheuristic algorithm for continuous and integrated industrial optimization.
  • To improve the efficiency and effectiveness of optimization approaches for complex manufacturing operations.

Main Methods:

  • Proposing Dynamic-Opposite Learning Differential Evolution Moth-Flame Optimization (DOLDEMFO).
  • Integrating a dynamic opposite learning (DOL) strategy to refine the search region.
  • Incorporating differential evolution (DE) into the Moth-Flame Optimization (MFO) framework for enhanced efficiency.

Main Results:

  • DOLDEMFO demonstrated competitive and consistent performance on CEC benchmark functions.
  • The algorithm achieved strong results in two industrial case studies.
  • Comparative analysis confirmed DOLDEMFO's superiority over conventional metaheuristics.

Conclusions:

  • DOLDEMFO is an effective approach for addressing complex industrial optimization challenges.
  • The enhanced algorithm shows significant potential for improving industrial manufacturing processes.
  • The combination of DOL and DE within MFO offers a robust optimization framework.