Evolutionary Multiobjective Optimization: Current and Future Challenges

In this paper, we will briefly discuss the current state of the research on evolutionary multiobjective optimization, emphasizing the main achievements obtained to date. Achievements in algorithmic design are discussed from its early origins until the current approaches which are considered as the “second generation” in evolutionary multiobjective optimization. Some relevant applications are discussed as well, and we conclude with a list of future challenges for researchers working (or planning to work) in this area in the next few years.

[1]  Jonathan E. Fieldsend,et al.  Full Elite Sets for Multi-Objective Optimisation , 2002 .

[2]  W. Habenicht,et al.  Quad Trees, a Datastructure for Discrete Vector Optimization Problems , 1983 .

[3]  Andrzej Jaszkiewicz,et al.  Performance of Multiple Objective Evolutionary Algorithms on a Distribution System Design Problem - Computational Experiment , 2001, EMO.

[4]  Shapour Azarm,et al.  IMMUNE NETWORK SIMULATION WITH MULTIOBJECTIVE GENETIC ALGORITHMS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 2000 .

[5]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[6]  C. Coello,et al.  Multiobjective optimization using a micro-genetic algorithm , 2001 .

[7]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[8]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[9]  J. Dennis,et al.  A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems , 1997 .

[10]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[11]  Wayne Pullan Optimising multiple aspects of network survivability , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  D. Goldberg,et al.  A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data , 2001 .

[13]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[14]  Shigeru Obayashi,et al.  Multiobjective genetic algorithm applied to aerodynamic design of cascade airfoils , 2000, IEEE Trans. Ind. Electron..

[15]  Günter Rudolph,et al.  Convergence properties of some multi-objective evolutionary algorithms , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[17]  H. Kunzi,et al.  Lectu re Notes in Economics and Mathematical Systems , 1975 .

[18]  Lothar Thiele,et al.  Multiobjective genetic programming: reducing bloat using SPEA2 , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[19]  Ivo F. Sbalzarini,et al.  Microchannel Optimization Using Multiobjective Evolution Strategies , 2001, EMO.

[20]  P. Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[21]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[22]  John A. W. McCall,et al.  Multi-objective Optimisation of Cancer Chemotherapy Using Evolutionary Algorithms , 2001, EMO.

[23]  N Miyanaga,et al.  Spectroscopic determination of dynamic plasma gradients in implosion cores. , 2002, Physical review letters.

[24]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[25]  Carlos A. Coello Coello,et al.  Handling preferences in evolutionary multiobjective optimization: a survey , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[26]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[27]  G. Cowles Studies of Mascarene Island birds: The fossil record , 1987 .

[28]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[29]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[30]  Clarisse Dhaenens,et al.  A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop , 2001, EMO.

[31]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[32]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[33]  John J. Grefenstette,et al.  Genetic algorithms and their applications , 1987 .

[34]  Martina Gorges-Schleuter,et al.  Application of Genetic Algorithms to Task Planning and Learning , 1992, Parallel Problem Solving from Nature.

[35]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[36]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[37]  Joel N. Morse,et al.  Reducing the size of the nondominated set: Pruning by clustering , 1980, Comput. Oper. Res..

[38]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[39]  Jeffrey Horn,et al.  The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems , 2001, EMO.

[40]  Tughrul Arslan,et al.  An evolutionary algorithm for the multi-objective optimisation of VLSI primitive operator filters , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[41]  R. J. Balling,et al.  The maximin fitness function for multi-objective evolutionary computation: application to city planning , 2001 .

[42]  Thomas Hanne,et al.  On the convergence of multiobjective evolutionary algorithms , 1999, Eur. J. Oper. Res..

[43]  Yaochu Jin,et al.  Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how? , 2001 .