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A novel particle swarm optimization (NPSO) algorithm with dynamically changing inertia weight based on fltness and iterations was presented for improving the performance of the Particle Swarm Optimization algorithm. The new algorithm was tested with three benchmark functions. The experimental results show that the swarm can escape from local optimum, and it also can speed up the convergence of particles...
This paper proposes a formulation of the multi-depot vehicle routing problem (MDVRP) that is solved by the particle swarm optimization (PSO) algorithm. PSO is one of the evolutionary computation technique, motivated by the group organism behavior such as bird flocking or fish schooling. Compared with other search methods, such as genetic algorithm, ant colony optimization and simulated annealing algorithm,...
To overcome the premature caused by standard particle swarm optimization (PSO) algorithm searching for the large lost in population diversity for the little search space, a dynamically changing inertia weight PSO based on the average distance with the best previously visited position and the position of the best individual of the whole swarm is proposed. The algorithm can balance the trade-off between...
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