New evolutionary algorithm for dynamic multiobjective optimization problems

Authors:
Chun-an Liu;Yuping Wang
Affiliations:
School of Computer Science and Engineering, Xidian University, Xi'an, China;School of Computer Science and Engineering, Xidian University, Xi'an, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 2
Cited 1

Genetic algorithms in time-dependent environments

Theoretical aspects of evolutionary computing
Unified particle swarm optimization in dynamic environments

EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing

Benchmarks for dynamic multi-objective optimisation algorithms

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new evolutionary algorithm for dynamic multi-objective optimization problems(DMOPs) is proposed. first, the time period is divided into several equal subperiods. In each subperiod, the DMOPs is approximated by a static multi-objective optimization problem(SMOP). Second, for each SMOP, the static rank variance and the static density variance of the population are defined. By using the two static variance of the population, each SMOP is transformed into a static bi-objective optimization problem. Third, a new evolutionary algorithm is proposed based on a new mutation operator which can automatically check out the environment variation. The simulation results indicate the proposed algorithm is effective.