Dynamic Multi-objective Optimization Evolutionary Algorithm

Authors:
Chun-an Liu;Yuping Wang
Affiliations:
Baoji University of Arts and Sciences, China;Xidian University, China
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Year:
2007

Citing 0
Cited 4

Hybrid genetic algorithm for dynamic multi-objective route planning with predicted traffic in a real-world road network

Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

International Journal of Applied Evolutionary Computation
Benchmarks for dynamic multi-objective optimisation algorithms

ACM Computing Surveys (CSUR)
Solution attractor of local search in travelling salesman problem: concept, construction and application

International Journal of Metaheuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new evolutionary algorithm for Dynamic multi-objective optimization is proposed in this paper. First, the time period is divided into several random subperiods. In each subperiod, the problem is approximated by a static multi-objective optimization problem. Thus, the dynamic multi-objective optimization problem is approximately transformed into several static multiobjective problems. Second, for each static multiobjective optimization problem, the expected rank variance and the expected density variance of the population are firstly defined. By using the expected rank variance and the expected density variance of the population, the dynamic multiobjective optimization problem is transformed into a bi-objective optimization problem. Third, a new evolutionary algorithm is proposed based on a new self-check operator which can automatically check out the time variation. At last, the simulation is made and the results demonstrate the effectiveness of the proposed algorithm.