An Improved Differential Evolution for solving Large Scale Global Optimization

Differential evolution (DE) is a population-based optimization algorithm. The members of population in DE are called parameter vectors. Due to more real-world optimization problems become increasingly complex. Algorithms with more ability and efficiency for searching potential solution are also increasing in demand. Thus, in this paper, an improved DE is proposed for solving large scale global optimization. The proposed method is incorporated with the population manager to eliminate redundant parameter vectors or to hire new ones or to maintain population size according to the solution searching status to make the process more efficient. The proposed method also involves mutation and cross-over for prevent the solutions from falling into the local minimum and enhance searching ability.