Multi-objective Optimization Based on Improved Differential Evolution Algorithm
Shuqiang Wang, Jianli Ma · TELKOMNIKA (Telecommunication Computing Electronics and Control) · 2014
On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions.