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Improving Convergence Speed of Bat Algorithm Using Multiple Pulse Emissions along Multiple Directions.

Waqar Younas1, Gauhar Ali2, Naveed Ahmad2

  • 1Department of Computer Science and Software Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan.

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|December 11, 2022
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Summary
This summary is machine-generated.

This study enhances the BAT Algorithm (BA) to improve its local search capabilities for optimization problems. The modified BA effectively avoids local optima, leading to better solutions for complex theoretical and realistic issues.

Keywords:
BAT algorithmfunction optimizationlevy flightspulse emission

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Metaheuristic algorithms are valuable for solving complex optimization problems by exploring solution spaces.
  • The BAT Algorithm (BA) is a popular metaheuristic but can get stuck in local optima, limiting its effectiveness.
  • Addressing local optima and premature convergence is crucial for enhancing optimization algorithm performance.

Purpose of the Study:

  • To improve the local searching ability of the BAT Algorithm (BA).
  • To reduce the premature convergence problem in the BAT Algorithm.
  • To develop a more robust optimization method for complex theoretical and realistic problems.

Main Methods:

  • An improved search equation was developed to incorporate more information during solution generation.
  • The enhanced BAT Algorithm was tested using a standard set of benchmark functions.
  • Performance was evaluated by comparing the proposed method against existing algorithms.

Main Results:

  • The improved BAT Algorithm demonstrated enhanced local searching capabilities.
  • The modifications successfully diminished the issue of premature convergence.
  • Simulation results indicated that the proposed method yields superior optimal solutions compared to other approaches.

Conclusions:

  • The enhanced BAT Algorithm offers improved performance in tackling optimization challenges.
  • The modifications provide a more effective strategy for avoiding local optima.
  • This research contributes a refined metaheuristic approach for complex problem-solving.