Basic Particle Swarm Optimization Algorithm MATLAB Implementation
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Resource Overview
MATLAB source code for the fundamental Particle Swarm Optimization algorithm with customizable fitness function, providing a flexible framework for optimization problems
Detailed Documentation
This documentation presents the MATLAB source code implementation of the basic Particle Swarm Optimization (PSO) algorithm. The fitness function within the code can be modified according to your specific optimization requirements. You can further enhance the algorithm's performance by adjusting key parameters such as the number of particles, maximum iteration count, or incorporating advanced optimization techniques. The implementation includes core PSO components: particle initialization with random positions and velocities, fitness evaluation, personal best (pbest) tracking, global best (gbest) identification, and velocity/position update equations using cognitive and social parameters. This flexibility allows you to adapt the algorithm to various problem domains and achieve more accurate optimization results through proper parameter tuning and fitness function customization.
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