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Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review.

Sanjoy Chakraborty1,2, Apu Kumar Saha3, Absalom E Ezugwu4

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Differential evolution (DE) is a powerful optimization algorithm. This survey reviews recent DE advancements in parameter adaptation, mutation strategies, and multi-objective variants, highlighting its applications in image processing.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Differential evolution (DE) is a widely recognized population-based optimization algorithm known for its simplicity and effectiveness.
  • Its robustness and problem-solving capabilities have led to widespread adoption in academic and industrial settings.
  • Ongoing research focuses on enhancing DE's exploration and exploitation through novel mutation techniques and parameter tuning.

Purpose of the Study:

  • To provide a comprehensive overview of recent developments in Differential Evolution over the past twelve years.
  • To analyze advancements in parameter adaptation, parameter settings, and mutation strategies.
  • To explore hybridizations, multi-objective variants, and applications of DE in image processing.

Main Methods:

  • Literature review and synthesis of recent research on Differential Evolution.
  • Categorization of DE variants based on parameter adaptation, mutation strategies, and hybridization techniques.
  • Analysis of DE's performance and applications in solving image processing problems.

Main Results:

  • Significant progress has been made in adaptive parameter control and sophisticated mutation strategies for DE.
  • Hybrid DE algorithms demonstrate improved performance by combining DE with other optimization techniques.
  • DE and its variants have shown considerable success in addressing various image processing challenges.

Conclusions:

  • Differential Evolution continues to evolve, with recent advancements enhancing its efficiency and applicability.
  • The survey highlights the versatility of DE, particularly its impact on image processing tasks.
  • Future research directions include further refinement of adaptive mechanisms and exploration of new application domains for DE.