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To choose the appropriate value of inertia weight can improve the performance of PSO by means of making a good balance between exploration and exploitation in search process. This paper presents a novel inertia weight variation method based on a piecewise function, in which there are two parts: one is nonlinear decreasing to enhance the explorative ability; the other is linear decreasing just as standard...
An adaptive particle swarm optimization(APSO) algorithm is presented to solve the problem that the standard particle swarm optimization(PSO) algorithm is easy to fall into a locally optimized point, where inertia weight is nonlinearly adjusted by using population diversity information. Velocity mutation factor and position interchange factor are both introduced. The APSO algorithm thus improves its...
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,...
Particle Swarm Optimization (PSO) is a recently proposed population-based evolutionary algorithm, which shows good performance in many optimization problems. To achieve better performance, this paper presents a new variant of PSO algorithm called PSO with Hybrid Velocity Updating Strategies (HVS-PSO). HVS-PSO employs another two velocity updating strategies besides the original velocity updating strategy...
The Particle Swarm Optimization (PSO) plunges into the local minimum easily. In order to overcome this shortcoming, we propose an improved PSO algorithm with the features of linearly decreasing of inertia weight and the re-initialization of the particle when it gets stagnated. The improved PSO is a local PSO and its topology is wheels. From the experimental results of three non-linear testing functions...
The particle swarm optimization algorithm (PSO) has successfully been applied to many engineering optimization problems. However, the most of existing improved PSO algorithms work well only for small-scale problems on low-dimensional space. In this new self-adaptive PSO, a special function, which is defined in terms of the particle fitness, swarm size and the dimension size of solution space, is introduced...
Particle swarm optimization (PSO) as an efficient and powerful problem-solving strategy has been widely used, but the appropriate adjustment of its inertia weight usually requires a lot of time and labor. In this paper, a nonlinear variation strategy to inertia weight is presented. The results obtained through the proposed method are compared with existing PSO algorithms. Finally, the simulation results...
This paper modified the structure of the original PSO algorithm. It proposes that the particles' position have relationship with the one particle's and the whole swarm's perceive extent in the processing of this time and last time, and presents the inertial weight based on simulated annealing temperature. So a new Particle Swarm Optimization algorithm (NPSO) is proposed. It can not improve the one...
The particle swarm optimization algorithm (PSO) has successfully been applied to many engineering optimization problems. However, most of the existing improved PSO algorithms work well only for small-scale problems. In this new self-adaptive PSO, a special function, which is defined in terms of the particle fitness and swarm size, is introduced to adjust the inertia weight adaptively. In a given generation,...
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