Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment

Authors:
Yu Wang;Bin Li
Affiliations:
Natural Inspired Computation and Application Laboratory, Department of electronic science and technology, University of Science and Technology of China;Natural Inspired Computation and Application Laboratory, Department of electronic science and technology, University of Science and Technology of China
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 10
Cited 1

Population-based incremental learning with memory scheme for changing environments

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Experimental study on population-based incremental learning algorithms for dynamic optimization problems

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multiobjective Optimization in Bioinformatics and Computational Biology

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications

IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey

IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms for electric power dispatch problem

IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization

IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm

IEEE Transactions on Evolutionary Computation

Benchmarks for dynamic multi-objective optimisation algorithms

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

As the research of dynamic optimization arising, memory-based strategy has gained public attention recently. However, few studies on developing dynamic multi-objective optimization algorithms and even fewer studies on multi-objective memory-based strategy were reported previously. In this paper, we try to address such an issue by proposing several memory-based multi-objective evolutionary algorithms and experimentally investigating different multi-objective dynamic optimization schemes, which include restart, explicit memory, localsearch memory and hybrid memory schemes. This study is to provide pre-trial research of how to appropriately organize and effectively reuse the changed Pareto-optimal decision values (i.e., Pareto-optimal solutions: POS) information.