scholarly journals A Novel Fuzzy Clustering Recommendation Algorithm Based On Pso

2014 ◽  
Vol 14 (5) ◽  
pp. 108-117 ◽  
Author(s):  
Zhang Hui ◽  
You Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Search Space ◽  
Improved Method ◽  
Recommendation Algorithm ◽  
Swarm Optimization ◽  
Fuzzy Clustering Algorithm

Abstract Aiming at the problem of recommendation systems, this paper proposes a fuzzy clustering algorithm based on particle swarm optimization. This algorithm can find the best solution, using the capacity of global search in PSO algorithm with a powerful global and defining a proportion factor, which can adjust the position and reduce the search space automatically. Then using mutation particles it replaces the particles flying out the solution space by new particles during the searching process. In order to check the performance of the proposed algorithm, by testing with typical ZDT1, ZDT2, ZDT3 functions, the experimental results show that the improved method not only has a better ability to converge to the global point, but can also efficiently avoid premature convergence.

Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application

2010 ◽  
Vol 44-47 ◽  
pp. 4067-4071 ◽  
Author(s):  
Xue Yong Li ◽  
Jia Xia Sun ◽  
Jun Hui Fu ◽  
Guo Hong Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Space ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Clustering Algorithm

A fuzzy clustering algorithm based on improved particle swarm optimization was proposed in this paper. First reduce dimension of solution space, separate it into smaller solution space. In separated solution space, use of improved particle swarm optimization algorithm to search the sub-optimal solution as a chromosome of whole particle,use improved PSO to search global optimal solution. The particle solve the problem that swarm algorithm easy to fall into local optimal solution in high dimensional space, and the problem that the fuzzy clustering algorithm is sensitive to initial value problems. Simulation results show the effectiveness of this algorithm.

Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection

2012 ◽  
pp. 182-205 ◽  
Author(s):  
Ying Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Spam Detection ◽  
Swarm Optimization ◽  
Hole Model ◽  
Successive Generations ◽  
Benchmark Test Functions

Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunity-clonal strategies are presented for their rapid convergence, easy implementation and ability of optimization. A novel PSO algorithm, clonal particle swarm optimization (CPSO) algorithm, is proposed based on clonal principle in natural immune system. By cloning the best individual of successive generations, the CPSO enlarges the area near the promising candidate solution and accelerates the evolution of the swarm, leading to better optimization capability and faster convergence performance than conventional PSO. As a variant, an advance-and-retreat strategy is incorporated to find the nearby minima in an enlarged solution space for greatly accelerating the CPSO before the next clonal operation. A black hole model is also established for easy implementation and good performance. Detailed descriptions of the CPSO algorithm and its variants are elaborated. Extensive experiments on 15 benchmark test functions demonstrate that the proposed CPSO algorithms speedup the evolution procedure and improve the global optimization performance. Finally, an application of the proposed PSO algorithms to spam detection is provided in comparison with the other three methods.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

Optimization Design of Gravity Dam Section Based on PSO Algorithm

2012 ◽  
Vol 424-425 ◽  
pp. 535-539
Author(s):  
Liang Ming Hu ◽  
Yi Zhi Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Gravity Dam ◽  
Actual Situation ◽  
Swarm Optimization

Particle Swarm Optimization (PSO) algorithm is a technique for optimization based on iteration, which initializes system to product a series of random solutions, in this solution space, particles commit themselves to search for a better solution and in the final the optimal one is found. Applying this algorithm to the design of gravity dam section then we find: PSO, as shown by the example given in this paper, is an available algorithm which is not only tally with the actual situation, but safe and economical. So, PSO provides a new idea and method for optimization design of gravity dam section.

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