Study of Simulation Test Generation Method Based on Improved Binary Particle Swarm Optimization Algorithm

2013 ◽  
Vol 823 ◽  
pp. 661-664
Author(s):  
Guang Yao Lian ◽  
Peng Cheng Yan ◽  
Jiang Sheng Sun ◽  
Kao Li Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Test Generation ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Binary Particle Swarm Optimization ◽  
Simulation Test ◽  
Swarm Optimization

To solve the backdating problem of traditional test generation methods, it puts forward a new test generation method based on improved binary particle swarm optimization algorithm in the paper. It estates the fitness function of test vector and faults in the circuits, and the optimal solution is the maximal value of the function. The experimentations prove that the method can reduce the compute quantity of test generation.

scholarly journals Research on Timetabling Problems Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 736-741
Author(s):  
Xin Min Ma ◽  
Lin Li Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Educational System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Course Timetabling ◽  
Fitness Value ◽  
Course Timetabling Problem

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

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.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

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