Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization

Area of Science:

• Computational intelligence
• Optimization algorithms
• Heuristic methods

Background:

• Particle swarm optimization (PSO) and genetic algorithms (GA) are widely used for complex optimization.
• PSO often converges to local optima due to favoring exploitation.
• GA can overcome local extrema but suffers from slow convergence.

Purpose of the Study:

• To propose a novel hybrid algorithm integrating PSO within a GA framework.
• To enhance the balance between exploration and exploitation in optimization.
• To improve the performance of heuristic optimization techniques.

Main Methods:

• Nesting particle swarm optimization operations within a genetic algorithm.
• Creating a hybrid approach with a general population and a sub-population.
• Testing the algorithm on continuous and discrete (traveling salesman) optimization problems.

Main Results:

• The hybrid algorithm demonstrates a superior balance between exploration and exploitation.
• Achieves consistently accurate results compared to parent algorithms and existing hybrids.
• Shows relatively small computational cost for effective optimization.

Conclusions:

• The proposed hybrid algorithm offers improved performance over standalone PSO and GA.
• It provides a robust solution for diverse continuous and discrete optimization tasks.
• This approach represents a significant advancement in heuristic optimization strategies.