Abstract
This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
J. J. Grefenstette (1999) ArticleTitleEvolvability in Dynamic Fitness Landscapes: A Genetic Algorithm Approach Proceedings of the 1999 Congress on Evolutionary Computation 3 1999–2038
I. Karcz-Duleba (2001) ArticleTitleDynamics of Infinite Populations Evolving in a Landscape of uni- and Bi-modal Fitness Functions IEEE Transactions on Evolutionary Computation 5 IssueID4 398–409
P. D. Stroud (2001) ArticleTitleKalman-extended Genetic Algorithm for Search in Nonstationary Environments with Noisy Fitness Evaluations IEEE Transactions on Evolutionary Computation 5 IssueID1 66–77
Oh Sang-Keon Lee Choon-Young Lee Ju-Jang (2002) ArticleTitleA New Distributed Evolutionary Algorithm for Optimization in Nonstationary Environments Proceedings of the 2002 Congress on Evolutionary Computation 1 378–383
Ghosh, A., Tsutsui, S. & Tanaka, H. (1998). Function Optimization in Nonstationary Environment using Steady State Genetic Algorithms with Aging of Individuals. Proceedings of the 1998 IEEE International Conference on Evolutionary Computation 666–671
W. Liles K. De Jong (1999) ArticleTitleThe Usefulness of Tag Bits in Changing Environments Proceedings of the 1999 Congress on Evolutionary Computation 3 1999–2060
Z. Michalewicz (1996) Genetic Algorithm + Data Strucutres = Evolution Programs Springer-Verlag Press Berlin
P. L. Yu (1985) Multiple Criteria Decision Making: Concepts, Techniques and Extensions Plenum New York
K. Deb A. Pratap S. Agarwal T. Meyarivan (2002) ArticleTitleA Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II IEEE Transaction on Evolutionary Computation 6 IssueID2 182–197
N. Srinivas K. Deb (1994) ArticleTitleMultiobjective Optimization using Non-dominated Sorting in Genetic Algorithms Evolutionary Computation 2 IssueID3 221–248
E. Zitzler L. Thiele (1999) ArticleTitleMultiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach IEEE Transactions on Evolutionary Computation 3 IssueID4 257–271
Zitzler, E. & Thiele, L. (1998). An Evolutionary Algorithm for Multi-objective Optimization: The Strength Pareto Approach. Swiss Federal Institute of Technology, TIK-Report, No. 43
J. D. Knowles D. W. Corne (2000) ArticleTitleApproximating the Nondominated Front using the Pareto Archived Evolution Strategy Evolutionary Computation 8 IssueID2 149–172
M. Laumanns G. Rudolph H. P. Schwefel (1998) ArticleTitleA Spatial Predator–Prey Approach to Multi-objective Optimization Parallel Problem Solving from Nature 5 241–249
C. M. Fonseca P. J. Fleming (1998a) ArticleTitleMultiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms. I. A Unified Formulation IEEE Transactions on Systems, Man and Cybernetics, Part A 28 IssueID1 26–37
C. M. Fonseca P. J. Fleming (1998b) ArticleTitleMultiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms. II. Application Example IEEE Transactions on Systems, Man and Cybernetics, Part A 28 IssueID1 38–47
Tamaki, H., Kita, H. & Kobayashi, S. (1996). Multi-objective Optimization by Genetic Algorithms: A Review. Proceedings of IEEE International Conference on Evolutionary Computation
Fonseca, C. M. & Fleming, P. J. (1993). Multiobjective Genetic Algorithms. Genetic Algorithms for Control Systems Engineering, IEE Colloquium
Schaffer, J. D. (1985). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Proceedings of the 1st IEEE International Conference on Genetic Algorithms 93–100
Kursawe, F. (1991). A Variant of Evolution Strategies for Vector Optimization. Parallel Problem Solving from Nature, Springer, 193–197
Fonseca, C. M. & Fleming, P. J. (1985). Genetic Algorithms for Multi-objective Optimization: Formulation, Discussion and Generalization. Proc. of the 5th International Conference on Genetic Algorithms 141–153
Goldberg, D. E. & Richardson, J. (1987). Genetic Algorithms with Sharing for Multimodal Function Optimization. Proceedings of the Second International Conference on Genetic Algorithm 41–49
Knowles, J. D. & Corne, D. W. (2002). On Metrics for Comparing Non-dominated Sets. In Congress on Evolutionary Computation. Proceedings of the 2002 Congress on Evolutionary Computation 1: 723–728
J. D. Knowles D. W. Corne M. J. Oates (2000) On the Assessment of Multiobjective Approaches to the Adaptive Distributed Database Management Problem M. Schoenauer (Eds) et al. Parallel Problem Solving from Nature – PPSN VI Springer Berlin 869–878
Hansen, M. P. & Jaszkiewicz, A. (1998). Evaluating the Quality of Approximations to the Nondominated Set Technical report, Institute of Mathematical Modelling, Technical University of Denmark, IMM-REP-1998-7
E. Zitzler K. Deb L. Thiele (2000) ArticleTitleComparison of Multiobjective Evolutionary Algorithms: Empirical Results Evolutionary Computation 8 IssueID2 173–195
Farhang-Mehr, A. & Azarm, S. (2002). Diversity Assessment of Pareto Optimal Solution Sets: An Entropy Approach. Proceedings of the 2002 Congress on Evolutionary Computation 1: 723–728
Branke, Juergen, (2001). Pareto-Front Exploration with Uncertain Objectives. Proc. First International Conference on Evolutionary Multi-Criterion Optimization, Zurich, Switzerland, March 7–9, 2001. In Lecture Notes in Computer Science (LNCS), 1993: 314–328, Springer
Zitzler, E. (1999). Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Ph.D. Thesis, Swiss Federal Institute of Technology Zurich
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
GUAN, SU., CHEN, Q. & MO, W. Evolving Dynamic Multi-Objective Optimization Problems with Objective Replacement. Artif Intell Rev 23, 267–293 (2005). https://doi.org/10.1007/s10462-004-5900-6
Issue Date:
DOI: https://doi.org/10.1007/s10462-004-5900-6