How to solve it: modern heuristics
How to solve it: modern heuristics
Journal of Global Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
How to detect global optimums of the complex function is of vital importance in diverse scientific fields. Though stochastic optimization strategies simulating evolution process are proved to be valuable tools, the balance between exploitation and exploration of which is difficult to be maintained. In this paper, some established techniques to improve the performance of evolutionary computation are discussed firstly, such as uniform design, deflection and stretching the objective function, and space contraction. Then a novel scheme of evolutionary algorithms is proposed to solving the optimization problems through adding evolution operations to the searching space contracted regularly with these techniques. A typical evolution algorithm differential evolution is chosen to exhibit the new scheme's performance and the experiments done to minimize the benchmark nonlinear optimization problems and to detect nonlinear map's unstable periodic points show the put approach is very robust.