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
