Hybrid Swarming Algorithm With Van Der Waals Force.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• Premature convergence to local optima is a common challenge in swarm intelligence algorithms.
• Existing algorithms often struggle with balancing exploration and exploitation effectively.
• The integration of biological principles can lead to novel optimization strategies.

Purpose of the Study:

• To propose a novel hybrid swarming algorithm integrating Ant Colony Optimization (ACO) and Physarum Polycephalum (PP) algorithm.
• To enhance the pheromone update mechanism by incorporating the Van Der Waals force to mitigate premature convergence.
• To evaluate the performance of the proposed algorithm in terms of accuracy and convergence speed.

Main Methods:

• Development of a hybrid algorithm combining ACO and PP.
• Implementation of a modified pheromone update rule utilizing Van Der Waals force principles.
• Comparative analysis against state-of-the-art optimization algorithms.
• Validation using a real-world case study involving the capitals of 35 Asian countries.

Main Results:

• The hybrid algorithm effectively prevents premature convergence to local optima.
• Demonstrated superior solving accuracy compared to existing literature algorithms.
• Achieved significantly reduced convergence time.
• Verified robustness and versatility through a practical application.

Conclusions:

• The proposed hybrid swarming algorithm offers a robust and efficient solution for complex optimization problems.
• The integration of Van Der Waals force in pheromone updates is a key innovation for enhanced performance.
• The algorithm shows strong potential for real-world applications requiring high accuracy and speed.