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Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design.

Xinru Li1, Zihan Lin1, Haoxuan Lv1

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This study introduces PSMADE, an improved slime mould algorithm (SMA) that enhances optimization speed and solution quality by integrating differential evolution and the Powell mechanism. PSMADE demonstrates superior performance on complex engineering problems.

Keywords:
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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • The slime mould algorithm (SMA) is a population-based swarm intelligence algorithm inspired by slime mould foraging behavior.
  • SMA suffers from slow convergence and premature convergence, limiting its effectiveness on complex optimization tasks.

Purpose of the Study:

  • To propose an improved slime mould algorithm, named PSMADE, to address the limitations of the original SMA.
  • To enhance the global and local search capabilities of the slime mould algorithm.

Main Methods:

  • PSMADE integrates the differential evolution (DE) algorithm's crossover and mutation operations to improve individual diversity and global search.
  • The Powell mechanism with a taboo table is incorporated to strengthen local search and accelerate convergence.
  • Performance is evaluated using the CEC 2014 benchmark functions and four real-world constrained engineering problems.

Main Results:

  • PSMADE demonstrated significantly improved performance compared to 14 metaheuristic algorithms and 15 improved metaheuristic algorithms.
  • Experimental results confirmed that PSMADE effectively overcomes the slow convergence and premature convergence issues of the original SMA.
  • The proposed algorithm showed outstanding performance in solving complex optimization problems.

Conclusions:

  • PSMADE offers a robust and effective enhancement to the slime mould algorithm.
  • The integration of DE and the Powell mechanism provides a powerful approach for improving swarm intelligence optimization.
  • PSMADE shows significant potential as a valuable tool for solving complex real-world engineering problems.