Explore MATLAB source code curated for "组合优化问题" with clean implementations, documentation, and examples.
The Shuffled Frog Leaping Algorithm (SFLA) is a novel optimization technique in evolutionary computing, originally introduced by Eusuff and Lansey in 2003 to address combinatorial optimization problems. This population-based metaheuristic mimics frog behavior with memetic evolution and cooperative search strategies.
A program integrating Genetic Algorithm with Partial Least Squares (PLS) for variable screening, designed to solve combinatorial optimization problems with excessive variables through intelligent feature selection and optimization techniques.
The Ant Colony Algorithm is a probabilistic technique for solving combinatorial optimization problems. The core concept adapts behaviors from ant pheromone communication, which serves as an indirect form of environmental modification through stigmergy. In practical implementations, artificial ants communicate by detecting and modifying virtual pheromone trails, with key algorithmic components including pheromone initialization, evaporation mechanisms, and probabilistic path selection.
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.