Name
Abstract | ||
|---|---|---|
In the later period of optimization by particle swarm optimization (PSO) algorithm, the diversity scarcity of population easily causes the algorithm fall into the local optimum. Therefore, an improved PSO (IPSO) algorithm is presented, in which each particle has the ability of keeping its inertia motion and learning from another randomly selected particle with better performance; moreover, for the particle with better performance, the inertia weight will be larger and the learning coefficient will be smaller. Thus, for the particles sorted in order of decreasing performance, the inertia weights are decreased and the learning rate coefficients are increased gradually. The new learning approach develops the diversity of the population, while the new parameters setting approach develops the adaptability of the population. Comparison results with the basic PSO on the examination of some well-known benchmark functions show that the IPSO algorithm has higher searching speed and stronger global searching ability. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CEC.2009.4983061 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
adaptive random learning approach,improved particle swarm optimizer,new learning approach,inertia weight,learning (artificial intelligence),algorithm fall,ipso algorithm,particle swarm optimisation,basic pso,better performance,improved pso,inertia motion,diversity scarcity,particle swarm optimization,dynamic range,learning artificial intelligence,algorithm design and analysis,convergence,stability analysis,automation,optimization,topology,evolutionary computation | Particle swarm optimization,Convergence (routing),Adaptability,Population,Mathematical optimization,Algorithm design,Local optimum,Computer science,Evolutionary computation,Artificial intelligence,Inertia,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-2959-2 | 4 | 0.56 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziyang Zhen | 1 | 32 | 5.26 |
| Dao Bo Wang | 2 | 21 | 5.92 |
| Meng Li | 3 | 4 | 0.56 |