Multi-objective Optimization Based on Improved Differential Evolution Algorithm

Abstract: 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.
Keywords: differential evolution, effect of parameters, numerical experiment, multi-objective optimization
Author: Shuqiang Wang
Journal Code: jptkomputergg140119

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