Name
Abstract | ||
|---|---|---|
In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baldwinian operator to simulate the learning mechanism among the particles and employs the information of the swarm to alter the search space adaptively. Secondly, a mutation operation is introduced to make the particles leap the local optimum and enhance the chance to find out the global optimum. Finally, the proposed BLPSO is used to solve some complex optimization problems, the experiment results illustrate the efficiency of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CASoN.2010.73 | CASoN |
Keywords | Field | DocType |
baldwinian operator,hybrid algorithm,search space adaptively,novel hybrid algorithm,learning (artificial intelligence),experiment result,pso,particle swarm optimisation,particle swarm optimization,complex optimization problem,baldwinian learning,premature convergence,mutation operation,proposed blpso,learning mechanism,search space,acceleration,convergence,algorithm design and analysis,optimization problem,artificial neural networks,learning artificial intelligence,optimization | Particle swarm optimization,Mathematical optimization,Hybrid algorithm,Algorithm design,Swarm behaviour,Premature convergence,Local optimum,Computer science,Artificial intelligence,Artificial neural network,Optimization problem,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-8785-1 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wan-Liang Wang | 1 | 235 | 39.16 |
| Lili Chen | 2 | 1 | 2.73 |
| Jing Jie | 3 | 10 | 1.95 |
| Haiyan Wang | 4 | 10 | 6.38 |
| Xinli Xu | 5 | 79 | 10.92 |