Explore MATLAB source code curated for "混合粒子群算法" with clean implementations, documentation, and examples.
This implementation solves Traveling Salesman Problems (TSP) using a hybrid PSO approach with a built-in GUI interface. The system incorporates 9 distinct algorithms capable of handling various TSP scenarios with excellent performance, featuring algorithmic improvements for enhanced efficiency and solution quality.
The multi-objective goods loading problem represents a complex combinatorial optimization challenge classified as NP-hard. This paper employs a hybrid particle swarm optimization (PSO) algorithm enhanced with genetic algorithm strategies to solve this problem. The hybrid approach integrates crossover and mutation operations from genetic algorithms into the standard PSO framework, preventing premature convergence to local optima while accelerating convergence toward global optimum solutions. Furthermore, the implementation incorporates weight coefficients to balance multiple objectives, ensuring relatively optimal outcomes across all target functions through strategic parameter tuning.
Comprehensive collection of particle swarm optimization algorithms including standard PSO, hybrid PSO, and improved PSO variants. Easily customizable parameters with implementation details for optimization, search, and simulation applications.