research-article

A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization

Authors:

Jingjing MaAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 423 - 430

https://doi.org/10.1145/1830483.1830565

Published: 07 July 2010 Publication History

Get Access

Abstract

Real-world optimization involving multiple objectives in changing environment known as dynamic multi-objective optimization (DMO) is a challenging task, especially special regions are preferred by decision maker (DM). Based on a novel preference dominance concept called sphere-dominance and the theory of artificial immune system (AIS), a sphere-dominance preference immune-inspired algorithm (SPIA) is proposed for DMO in this paper. The main contributions of SPIA are its preference mechanism and its sampling study, which are based on the novel sphere-dominance and probability statistics, respectively. Besides, SPIA introduces two hypermutation strategies based on history information and Gaussian mutation, respectively. In each generation, which way to do hypermutation is automatically determined by a sampling study for accelerating the search process. Furthermore, The interactive scheme of SPIA enables DM to include his/her preference without modifying the main structure of the algorithm. The results show that SPIA can obtain a well distributed solution set efficiently converging into the DM's preferred region for DMO.

References

[1]

Jurgen Branke. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht, 2002.

Digital Library

Google Scholar

[2]

K. Deb, U. B. Rao N., S. Karthik. Dynamic Multi-Objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-Thermal Power Scheduling. KanGAL Technical Report 2006008.

Google Scholar

[3]

A. M. Zhou, Y. C. Jin, Q. F. Zhang, B. Sendhoff, E. Tsang. Prediction-based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization. In Conference on Evolutionary Multi-Criterion Optimization. pp. 832--846, Springer Berlin / Heidelberg, 2007.

Digital Library

Google Scholar

[4]

A. P. Wierzbicki, The use of reference objectives in multiobjective optimization, In: G. Fandel, T. Gal (Eds.), Multiple Criteria Decision Making Theory and Application. Springer-Verlag, New York, 469--486, 1980.

Google Scholar

[5]

A. Jaszkiewicz, R. Slowinski, The light beam search approach - an overview of methodology and applications, European Journal of Operational Research. 113(2) 300--314, 1999.

Crossref

Google Scholar

[6]

L. Thiele, K. Miettinen, P. J. Korhonen, J. Molina. A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization. Evolut Comput, MIT, 17(3), 411--436, 2009.

Digital Library

Google Scholar

[7]

K. Deb, and A. Kumar. Interactive Evolutionary Multi-Objective Optimization and Decision-Making using Reference Direction Method. KanGAL Report No. 2007001. January, 2007.

Google Scholar

[8]

Garrett S M, How do we evaluate artificial immune systems? IEEE Trans Evolut Comput, 13(2):145--178, 2005.

Digital Library

Google Scholar

[9]

K. Deb, A. Pratap, S. Agarwal. A fast and elitist multiobjective genetic algorithm: NSGA-II,IEEE Trans Evolut Comput, 6(2):182--197, 2002.

Digital Library

Google Scholar

[10]

J. Molina, L.V. Santana, A.G. Hernandez-Diaz, C. Coello, R. Caballero. g-dominance: Reference point based dominance for MultiObjective Metaheuristics. European Journal of Operational Research, 197-2: 685--692, 2009.

Crossref

Google Scholar

[11]

M. Farina, K. Deb, and P. Amato. Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans on Evol Comp, vol. 8, pp. 425--442, 2004.

Digital Library

Google Scholar

[12]

Van Veldhuizen, D. Multiobjective Evolutionary Algorithms: Classifications, Analysis, and New Innovations, Ph.D. Thesis, Dayton, OH: Air Force Institute of Technology. 1999.

Digital Library

Google Scholar

[13]

C. A. Coello Coello, G. B. Lamont, D. A. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems. 2nd ed., Springer, New York, 2007

Digital Library

Google Scholar

Cited By

View all

Liu HXia H(2023)Dynamic Multi-Objective Optimization for Gas Turbine Operation2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240776(1872-1877)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240776
Yang CDing JJin YChai T(2023)A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic OptimizationEvolutionary Computation10.1162/evco_a_0033231:4(433-458)Online publication date: 1-Dec-2023
https://doi.org/10.1162/evco_a_00332
Jiang SZou JYang SYao X(2022)Evolutionary Dynamic Multi-objective Optimisation: A SurveyACM Computing Surveys10.1145/352449555:4(1-47)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1145/3524495
Show More Cited By

Index Terms

A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Theory of computation
  1. Design and analysis of algorithms

Recommendations

An r-dominance-based preference multi-objective optimization for many-objective optimization

Evolutionary multi-objective optimization (EMO) algorithms have been used in finding a representative set of Pareto-optimal solutions in the past decade and beyond. However, most of Pareto domination-based multi-objective optimization evolutionary ...
An Updated Performance Metric for Preference-Based Evolutionary Multi-Objective Optimization Algorithms
GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference

Evolutionary multi-objective optimization (EMO) algorithms are widely used to solve problems involving multiple conflicting objectives. In general, these problems result in a well-distributed and diverse set of Pareto-optimal solutions, consisting of ...
Preference-based multi-objective evolutionary algorithm with linear combination scalarizing function and reference point adjustment
Abstract
In practice, the decision-maker (DM) may be only interested in a particular part of Pareto optimal front (PF). For this reason, many preference-based multi-objective evolutionary algorithms (MOEA) have been proposed to find a solution set that ...
Graphical abstract

Display Omitted
Highlights
- A family of new scalarizing functions are proposed to improve the preference-based MOEAs.
- A reference point adjustment method is proposed to control the size of ROI.
- Furthermore, the proposed algorithm is extended to solve the ...

Comments

Information & Contributors

Information

Published In

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '10

Sponsor:

SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
240
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)2

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Liu HXia H(2023)Dynamic Multi-Objective Optimization for Gas Turbine Operation2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240776(1872-1877)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240776
Yang CDing JJin YChai T(2023)A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic OptimizationEvolutionary Computation10.1162/evco_a_0033231:4(433-458)Online publication date: 1-Dec-2023
https://doi.org/10.1162/evco_a_00332
Jiang SZou JYang SYao X(2022)Evolutionary Dynamic Multi-objective Optimisation: A SurveyACM Computing Surveys10.1145/352449555:4(1-47)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1145/3524495
Xu DJiang MHu WLi SPan RYen G(2022)An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.311503626:4(690-703)Online publication date: Aug-2022
https://doi.org/10.1109/TEVC.2021.3115036
He QXiang ZRen P(2022)An environmental selection and transfer learning-based dynamic multiobjective optimization evolutionary algorithmNonlinear Dynamics10.1007/s11071-021-07180-x108:1(397-415)Online publication date: 12-Jan-2022
https://doi.org/10.1007/s11071-021-07180-x
Yu QZhong SLiu ZLin QHuang P(2020)Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment DetectionComplexity10.1155/2020/90538092020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/9053809
Liu RPeng LLiu JLiu J(2020)A diversity introduction strategy based on change intensity for evolutionary dynamic multiobjective optimizationSoft Computing10.1007/s00500-020-05175-1Online publication date: 14-Jul-2020
https://doi.org/10.1007/s00500-020-05175-1
Li QZou JYang SZheng JRuan G(2019)A predictive strategy based on special points for evolutionary dynamic multi-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3033-023:11(3723-3739)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3033-0
Chen RLi KYao X(2018)Dynamic Multiobjectives Optimization With a Changing Number of ObjectivesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.266963822:1(157-171)Online publication date: Feb-2018
https://doi.org/10.1109/TEVC.2017.2669638
Xiaogang Fu Jianyong Sun (2017)A new learning based dynamic multi-objective optimisation evolutionary algorithm2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969332(341-348)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969332
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

An r-dominance-based preference multi-objective optimization for many-objective optimization

An Updated Performance Metric for Preference-Based Evolutionary Multi-Objective Optimization Algorithms

Preference-based multi-objective evolutionary algorithm with linear combination scalarizing function and reference point adjustment