The Research of Building Fuzzy C-Means Clustering Model Based on Particle Swarm Optimization

Research on Kruskal Crossover Genetic Algorithm for Multi-Objective Logistics Distribution Path Optimization

“…When condition (1, 7) is introduced, the rules of minimum spanning tree cannot be met. Therefore, instead of giving up edge (1, 7), we use the algorithm to search for next one (4,8). As a result, the minimum spanning tree is established by adding the new edge (4,8 However, after add kruskal algorithm to the basic GA crossover operator, increased the overall complexity of the algorithm, and the corresponding algorithm run time consuming also increased.…”

“…Therefore, instead of giving up edge (1, 7), we use the algorithm to search for next one (4,8). As a result, the minimum spanning tree is established by adding the new edge (4,8 However, after add kruskal algorithm to the basic GA crossover operator, increased the overall complexity of the algorithm, and the corresponding algorithm run time consuming also increased. Therefore, this paper makes a further optimized to KCGA by the following methods, which will effectively reduce the algorithm running time consuming.…”

“…At present, the algorithms which are applied in logistics distribution path optimization mainly include ant colony algorithm (ACO) [2], genetic algorithm (GA) [3], particle swarm optimization (PSO) [4] and simulated annealing algorithm (SA) [5] and so on. Contrasting the algorithms mentioned above, based on biological evolution, GA starts searching from group and it can compare multiple individuals at the same time.…”