Research on Assembly Sequence Planning Based on Improved Slope One Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 3444-3449
Author(s):  
Yu Jin ◽  
Wen Jun Hou ◽  
Fu Xing Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Network Graph ◽  
Swarm Optimization ◽  
Sequence Planning

According to the characteristics and needs of complicated products, a method for handing assembly sequence based on improved Slope One algorithm was proposed. On the basis of that the network graph of assembly relationship was constructed, and a method for simplifying it was proposed by expressing elliptically same parts, identifying and hiding fasteners. In this paper, Slope One algorithm was initially introduced into the assembly sequence planning, and it was improved according to the problems to be resolved. In the meantime, particle swarm optimization algorithm was introduced into the feedback of the recommendation result. The method has been proved that it was not only used to obtain a good recommendation of assembly sequence but also sensitive to the individuation of designers.

scholarly journals Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

2016 ◽  
Vol 11 (1) ◽  
pp. 58-67 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Initial Population ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

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