Name
Abstract | ||
|---|---|---|
This paper mainly studies the influence of memory on individual performance in particle swarm system. Based on the observation of social phenomenon from the perspective of social psychology, the concept of individual memory contribution is defined and several measurement methods to determine the level of effect of individual memory on its behavior are discussed. A dynamic memory particle swarm optimization algorithm is implemented by dynamically assigning appropriate weight to each individual's memory according to the selected metrics values. Numerical experiment results on benchmark optimization function set show that the proposed scheme can effectively adjust the weight of individual memory according to different optimization problems adaptively. Numerical results also demonstrate that dynamic memory is an effective improvement strategy for preventing premature convergence in particle swarm optimization algorithm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1543834.1543843 | GEC Summit |
Keywords | Field | DocType |
different optimization problems adaptively,individual memory contribution,dynamic memory strategy,optimization algorithm,particle swarm system,dynamic memory particle swarm,individual performance,benchmark optimization,individual memory,particle swarm optimization algorithm,dynamic memory,premature convergence,particle swarm,particle swarm optimization,social psychology,optimization problem | Computer science,Artificial intelligence,Optimization problem,Metaheuristic,Particle swarm optimization,Dynamic random-access memory,Derivative-free optimization,Mathematical optimization,Premature convergence,Meta-optimization,Algorithm,Multi-swarm optimization,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiong Chen | 1 | 4 | 2.25 |
| Shengwu Xiong | 2 | 189 | 53.59 |
| Hongbing Liu | 3 | 59 | 8.74 |