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A fuzzy system based self-adaptive memetic algorithm using population diversity control for evolutionary

Brindha Subburaj1, S Miruna Joe Amali2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India. brindha.s@vit.ac.in.

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Summary

This study introduces the Fuzzy based Memetic Algorithm using Diversity control (F-MAD), a robust, self-adaptive evolutionary algorithm for multi-objective optimization. F-MAD demonstrates superior performance on benchmark problems, outperforming state-of-the-art methods without extensive parameter tuning.

Keywords:
Evolutionary computationsFuzzy systemMemetic algorithmMulti-objective optimizationPopulation diversity

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Evolutionary algorithms require extensive parameter tuning for diverse problem domains.
  • Existing methods often struggle with premature convergence and balancing exploration-exploitation.
  • Multi-objective optimization problems present significant challenges in finding optimal solutions.

Purpose of the Study:

  • To develop a robust and self-adaptive memetic algorithm for multi-objective optimization problems.
  • To enhance evolutionary algorithms by combining global and local search strategies.
  • To create an algorithm that adapts its parameters automatically, reducing the need for manual fine-tuning.

Main Methods:

  • Developed the Fuzzy based Memetic Algorithm using Diversity control (F-MAD).
  • Integrated Differential Evolution (DE) with a controlled local search procedure.
  • Employed fuzzy systems for self-adaptation of DE control parameters (crossover rate, scaling factor) to manage population diversity.

Main Results:

  • F-MAD demonstrated superior performance on CEC 2009 and DTLZ benchmark test problems.
  • Achieved better results than state-of-the-art algorithms on 8/10 CEC 2009 problems and all 7 DTLZ problems.
  • Statistical analysis (Friedman rank test) confirmed F-MAD's significant outperformance.

Conclusions:

  • F-MAD offers a robust and self-adaptive approach to multi-objective optimization.
  • The algorithm effectively balances exploration and exploitation, ensuring diversity and convergence.
  • F-MAD's adaptability makes it suitable for various application domains without parameter trial-and-error.