Differential evolution with improved population reduction

Authors:
Ming Yang;Zhihua Cai;Jing Guan;Wenyin Gong
Affiliations:
China University of Geosciences, Wuhan, China;China University of Geosciences, Wuhan, China;Huazhong University of Science and Technology, Wuhan, China;China University of Geosciences, Wuhan, China
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

Citing 4
Cited 0

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Exploring dynamic self-adaptive populations in differential evolution

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Population size reduction for the differential evolution algorithm

Applied Intelligence
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the Differential Evolution (DE), there are many adaptive DE algorithms proposed for parameter adaptation. However, they are mainly focus on the the mutation factor F and crossover probability CR. The adaptation of population size NP is not widely studied in the scope of DE. If reduce population size but not jeopardize performance of the algorithm significantly, it could reduce the number of evaluations for individuals and accelerate algorithm's convergence speed. This is beneficial to the optimization problems which need expensive evaluations. In this paper, we propose an improved population reduction method, considering the difference between individuals, and embed it into classic DE/rand/1/bin strategy, named dynNPMinD-DE. When population needs to reduce, select the best individual and the individuals with minimal-step difference vectors to form a new population. dynNPMinD-DE is applied to minimize a set of 13 scalable benchmark functions of dimensions D=30. The results show that compared with selecting better individuals and DE/rand/1/bin, dynNPMinD-DE can get better results on average, and the convergence becomes faster and faster as each population reduction.