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Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems.

V N Coelho1, I M Coelho2, M J F Souza3

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Evolutionary Computation
|June 4, 2016
PubMed
Summary

This study introduces a novel Evolution Strategy (ES) algorithm that self-adapts mutation operators for combinatorial optimization. The method effectively balances exploration and exploitation, demonstrating strong performance across diverse challenging problems.

Keywords:
Evolution strategiesmemetic algorithmsneighborhood structuresopen-pit mining operational planningreduced variable neighborhood searchshort-term load forecasting.unrelated parallel machine scheduling

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Area of Science:

  • Computational Intelligence
  • Operations Research
  • Artificial Intelligence

Background:

  • Combinatorial optimization problems are prevalent in various domains.
  • Existing algorithms often struggle with balancing exploration and exploitation.
  • Adaptive search strategies are crucial for complex problem-solving.

Purpose of the Study:

  • To present a novel Evolution Strategy (ES)-based algorithm for combinatorial optimization.
  • To develop a self-adaptive mechanism for mutation operators.
  • To integrate a Self-Adaptive Reduced Variable Neighborhood Search (RVNS) for guided search.

Main Methods:

  • The proposed algorithm combines Evolution Strategy (ES) with Self-Adaptive RVNS.
  • It utilizes a Greedy Randomized Adaptive Search Procedure (GRASP) for initial population generation.
  • The approach incorporates population-based learning, aligning with Memetic Algorithms.

Main Results:

  • The framework was successfully applied to three NP-Hard combinatorial optimization problems.
  • Computational results demonstrate effective convergence and adaptation of mutation strength.
  • The algorithm showed proficiency in combining diverse neighborhood structures for optimization.

Conclusions:

  • The proposed ES-based algorithm effectively solves challenging combinatorial optimization problems.
  • The self-adaptive mutation strategy enhances performance across different domains.
  • This evolutionary framework shows promise for future applications in optimization.