Improved Particle Swarm Algorithm and Its Application in Vehicle Routing Problem

2012 ◽  
pp. 269-274 ◽  
Author(s):  
Ding Pengxin ◽  
Zou Shurong ◽  
Zhang Hongwei
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Routing Problem ◽  
Swarm Algorithm

The Improvement of Hybrid Particle Swarm Algorithm and its Application

2011 ◽  
Vol 268-270 ◽  
pp. 798-802 ◽  
Author(s):  
Shu Rong Zou ◽  
Peng Xin Ding ◽  
Hong Wei Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Routing Problem ◽  
Multi Objective ◽  
Inertia Weight ◽  
Swarm Algorithm ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia

Hybrid multi-objective particle swarm algorithm is applied to vehicle routing problem and achieved good results, this paper based on the previous work, dynamic inertia weight is added to the particle swarm algorithm with intelligence factors, it improved the global search ability and the capacity of local convergence of the particle swarm algorithm; and the idea of immunity is introduced in the algorithm ,which makes the hybrid multi-objective particle swarm algorithm can effectively discard the repeated solutions in solving vehicle routing problems, this operation can improve the efficiency of the algorithm, and obtain better results under the same conditions.

scholarly journals An attractors-based particle swarm optimization for multiobjective capacitated vehicle routing problem

2021 ◽  
Author(s):  
Abdoul-hafar HALASSI BACAR ◽  
Rawhoudine Said Charriffaini
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Genetic Operators ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Bus Routing ◽  
Displacement Based ◽  
Capacitated Vehicle

In this paper, a new multiobjective discrete particle swarm algorithm is presented for the Capacitated vehicle routing problem. The binary algorithm integrates particle displacement based on local attractors, a crowding distance as elitism policy and genetic operators. The proposed approach is first implemented on a set of well-known benchmarks for single-objective capacitated vehicle routing problems and compared to results underlined in the literature. The obtained results demonstrate its ability to achieve the main optimization solution and sometimes prove its efficiency from other given techniques. Then, the approach is applied to an academical example for the multiobjective Urban Bus Routing Problem with Route Balancing.

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

2012 ◽  
Vol 253-255 ◽  
pp. 1369-1373
Author(s):  
Tie Jun Wang ◽  
Kai Jun Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cloud Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Adaptive Particle Swarm Optimization ◽  
Searching Ability

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

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