最优解 Resources

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A hybrid algorithm combining differential evolution, genetic algorithm, and particle swarm optimization for constrained optimization problems. This implementation successfully obtains optimal solutions for all 13 standard test functions from reference [7] (T.P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000). The algorithm features constraint handling through stochastic ranking and adaptive parameter tuning. For technical inquiries, please visit http://2shi.phphubei.com

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A comprehensive program implementation of Dijkstra's shortest path algorithm. Dijkstra's algorithm is a classic graph traversal method that calculates the shortest paths from a single source node to all other nodes in a weighted graph. Its core characteristic involves expanding outward layer by layer from the starting point until reaching the destination. While Dijkstra's algorithm guarantees optimal solutions, its computational efficiency decreases with larger graphs due to extensive node processing. This algorithm serves as fundamental content in technical courses like Data Structures, Graph Theory, and Operations Research, with implementations typically involving priority queues and relaxation operations for edge weight updates.

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Core Technology Particle Swarm Optimization (PSO) is a swarm intelligence algorithm simulating bird flocking behavior, sharing fundamental concepts with other optimization algorithms. In PSO, each particle represents a bird with initial position and velocity, continuously adjusting flight speed and direction during movement to ultimately locate optimal solutions. Chaos refers to complex nonlinear system behavior resembling randomness, highly sensitive to initial conditions, easily escaping local minima with rapid search capabilities. The core concept of Chaotic PSO involves applying chaotic perturbations to the global best (Gbest) particle during each iteration, using the perturbed position for particle updates to prevent position convergence and enable local search around global optima. Implementation typically involves logistic maps for chaos generation and velocity-position update equations with dynamic inertia weights.

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