Name
Abstract | ||
|---|---|---|
Population initialization, as an important step in population-based stochastic algorithm, can affect the convergence speed and the quality of solutions. Generally, random initialization is used to generate initial population when lacking priori information. This paper presents a new initialization method by applying space transformation search (STS) strategy to generate initial population. Experimental results on 8 well-known benchmark problems show that the population initialization based on STS outperforms traditional random initialization and opposition-based population initialization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.371 | ICNC (5) |
Keywords | Field | DocType |
convergence speed,traditional random initialization,new initialization method,new population initialization method,opposition-based population initialization,population-based stochastic algorithm,population initialization,space transformation search,important step,random initialization,initial population,optimization,particle swarm optimization,benchmark testing,random processes,opposition,convergence,data mining,evolutionary computation,probability density function,stochastic processes,initial value problems | Convergence (routing),Particle swarm optimization,Population,Mathematical optimization,Computer science,Evolutionary computation,Stochastic process,Artificial intelligence,Initialization,Probability density function,Machine learning,Benchmark (computing) | Conference |
Citations | PageRank | References |
12 | 0.55 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Wang | 1 | 277 | 17.29 |
| Zhijian Wu | 2 | 313 | 21.20 |
| Jing Wang | 3 | 329 | 39.05 |
| Xiaojian Dong | 4 | 14 | 0.90 |
| Yu Song | 5 | 356 | 52.74 |
| Cheng Chen | 6 | 12 | 0.55 |