Loading...
Please wait, while we are loading the content...
Similar Documents
Swarm directions embedded differential evolution for faster convergence of global optimization problems.
| Content Provider | CiteSeerX |
|---|---|
| Author | Ali, Musrrat Pant, Millie Abraham, Ajith Ahn, Chang Wook |
| Abstract | In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algorithm. This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence. The embedding of swarm directions to the basic DE algorithm is done with the help of a “switchover constant ” called α which keeps a record of the contraction of search space. The proposed HDE algorithm is tested on a set of 10 unconstrained benchmark problems and four constrained real life, mechanical design problems. Empirical studies show that the proposed scheme helps in improving the convergence rate of the basic DE algorithm without compromising with the quality of solution. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Basic De Algorithm Hde Algorithm Contracted Search Space Particle Swarm Optimization Present Study Mechanical Design Problem Swarm Direction Differential Evolution Convergence Rate Unconstrained Benchmark Problem Switchover Constant Real Life Antagonist Factor New Hybrid Version Hybrid De Empirical Study Continuous Global Optimization Problem Search Space |
| Content Type | Text |
