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MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization

Mohammad H Nadimi-Shahraki1,2,3, Shokooh Taghian1,2, Hoda Zamani1,2

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The novel multi-trial vector-based Monkey King Evolution (MMKE) algorithm enhances optimization by combining strategies. MMKE demonstrates superior performance in complex engineering and power flow problems compared to existing methods.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Single evolution strategies in Monkey King Evolution (MKE) limit convergence and exploration-exploitation balance.
  • Collaborating multiple strategies can significantly improve algorithm performance.

Purpose of the Study:

  • To propose a multi-trial vector-based Monkey King Evolution (MMKE) algorithm.
  • To enhance global search capability and balance exploration-exploitation.
  • To prevent premature convergence in optimization problems.

Main Methods:

  • Introduced novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP).
  • Employed a multi-trial vector approach for strategy collaboration.
  • Evaluated performance on CEC 2018 test functions and real-world engineering problems.

Main Results:

  • MMKE achieved competitive and superior results in accuracy and convergence rate.
  • Statistical analysis (Friedman test) confirmed MMKE's significant superiority.
  • MMKE effectively solved engineering design and optimal power flow problems.

Conclusions:

  • MMKE offers improved global search and exploration-exploitation balance.
  • The algorithm demonstrates strong applicability to real-world optimization challenges.
  • MMKE provides better solutions for single and multi-objective optimal power flow problems.