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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,...
Solving fixed point equation by traditional iterative algorithm not only has the very big relation with the initial point but also cannot satisfy parallel. In this paper, a niche particle swarm optimization is used to solve fixed point equation, which sufficiently exerted the advantage of particle swarm optimization such as group search, strong robustness and it satisfies the question of parallel...
Camera autocalibration from Kruppapsilas equations must solve the problem of finding the global minimum of a cost function which has many local minima. Due to the drawbacks of the traditional optimization algorithms in camera calibration, which include initial value sensitiveness, pool convergence and easily plunging into local minima, we study the application of the particle swarm optimization algorithm...
Particle swarm optimization with passive congregation (PSOPC) was a new variant by adding an attraction of passive congregation. However, the performance of PSOPC is not stable when solving the multimodal benchmarks. To overcome this shortcoming, a modified particle swarm optimization based on a Chinese archaism (PSOCA) is designed. The experimental results demonstrate much better performance of the...
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