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Entropy-Based Diversification Approach for Bio-Computing Methods.

Rodrigo Olivares1, Ricardo Soto2, Broderick Crawford2

  • 1Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile.

Entropy (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Shannon entropy-based technique to help nature-inspired algorithms escape stagnation in optimization problems. The method improves solution diversification and performance, particularly for bat optimization algorithms.

Keywords:
Shannon entropybio–computing methodsimproved global searchmultidimensional knapsack problem

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Nature-Inspired Computing

Background:

  • Nature-inspired computing designs algorithms from natural phenomena for complex problems.
  • Optimization algorithms require a balance between intensification and diversification to avoid local optima.
  • Stagnation occurs when optimization stalls before finding the global optimum.

Purpose of the Study:

  • To propose an efficient technique for detecting and escaping local optimum regions in optimization algorithms.
  • To enhance the diversification capabilities of population-based bio-inspired algorithms.
  • To address the stagnation problem in nature-inspired computing.

Main Methods:

  • Utilizing Shannon entropy to measure uncertainty and guide exploration.
  • Integrating the Shannon entropy component into particle swarm optimization, bat optimization, and black hole algorithms.
  • Evaluating performance on twenty challenging instances of the multidimensional knapsack problem.

Main Results:

  • The proposed Shannon entropy-based exploration approach effectively manages solution diversification.
  • Improved algorithms generated a better distribution of optimal values compared to native versions.
  • Bat optimization algorithms showed superior performance across all instances when enhanced with the Shannon exploration strategy.

Conclusions:

  • The Shannon entropy technique is a viable alternative for managing diversification in bio-inspired algorithms.
  • The enhanced algorithms demonstrate significant improvements, especially in addressing local optima and stagnation.
  • This approach offers a robust method for improving the efficiency and effectiveness of nature-inspired optimization techniques.