Multi-objective Optimization Based on Improved Differential Evolution Algorithm

Shuqiang Wang (Hebei University of Engineering)
Jianli Ma (Suburban Water and Power Supply Management Office of Handan City)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 12, No 4: December 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.