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A Hybrid Algorithm Based on Gravitational Search and Particle Swarm Optimization Algorithm to Solve Function Optimization Problems
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wang, Jie-Sheng Song, Jiang-Di |
| Copyright Year | 2016 |
| Abstract | Abstract—Gravitational search algorithm (GSA) is a swarm intelligence heuristic optimization algorithm based on the law of gravitation. Aiming at the disadvantage of poor local search ability and slow convergence speed in standard GSA, four improved GSA-PSO hybrid algorithm are proposed by introducing a small constant updating strategy in order to enhance the update ability of velocity, acceleration factor and the optimal individual location, where PSO strategy was used to optimize the position and velocity of the GSA. Through simulation experiments on typical test functions to verify its performance, the simulation results show that the optimal setup of GSA parameters can improve the convergence rate of the algorithm and improve the accuracy of the solution. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.engineeringletters.com/issues_v25/issue_1/EL_25_1_04.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
