Improved Bat Algorithm Based on Multipopulation Strategy of Island Model for Solving Global Function Optimization...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Heuristic Computing

Background:

• The bat algorithm (BA) is a nature-inspired heuristic optimization technique simulating bat echolocation.
• Existing bat algorithms face challenges in convergence speed and precision.
• Enhancing BA is crucial for complex global optimization tasks.

Purpose of the Study:

• To improve the search performance, convergence speed, and optimization precision of the bat algorithm.
• To introduce a novel bat algorithm incorporating chaotic maps and Levy flight.
• To develop a multipopulation parallel bat algorithm using an island model.

Main Methods:

• An improved bat algorithm was developed integrating chaotic maps and Levy flight search strategy.
• Optimal chaotic map operators were selected through simulation experiments.
• A multipopulation parallel bat algorithm based on the island model was proposed.

Main Results:

• The improved bat algorithm demonstrated enhanced convergence speed and optimization accuracy.
• Simulation experiments on typical test functions validated the algorithm's effectiveness.
• The multipopulation parallel approach further refined optimization capabilities.

Conclusions:

• The proposed improved bat algorithm effectively enhances convergence speed and accuracy.
• Integration of chaotic maps and Levy flight offers significant performance gains.
• The multipopulation parallel strategy provides a robust framework for advanced optimization.