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jMetal: A Java framework for multi-objective optimization

Authors:

Juan J. Durillo,

Antonio J. NebroAuthors Info & Claims

Advances in Engineering Software, Volume 42, Issue 10

Pages 760 - 771

https://doi.org/10.1016/j.advengsoft.2011.05.014

Published: 01 October 2011 Publication History

Abstract

This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems. jMetal includes a number of classic and modern state-of-the-art optimizers, a wide set of benchmark problems, and a set of well-known quality indicators to assess the performance of the algorithms. The framework also provides support to carry out full experimental studies, which can be configured and executed by using jMetal's graphical interface. Other features include the automatic generation of statistical information of the obtained results, and taking advantage of the current availability of multi-core processors to speed-up the running time of the experiments. In this work, we include two case studies to illustrate the use of jMetal in both solving a problem with a metaheuristic and designing and performing an experimental study.

References

[1]

Weise, T., Zapf, M., Chiong, R. and Nebro, A.J., Why is optimization difficult?. In: Chiong, R. (Ed.), Studies in computational intelligence, vol. 193/2009. Springer. pp. 1-50.

[2]

Glover, F.W. and Kochenberger, G.A., Handbook of metaheuristics. 2003. Kluwer.

[3]

Blum, C. and Roli, A., Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv. v35 i3. 268-308.

[4]

Deb, K., Multi-objective optimization using evolutionary algorithms. 2001. John Wiley & Sons.

[5]

Coello Coello, C.A., Lamont, G.B. and Van Veldhuizen, D.A., Evolutionary algorithms for solving multi-objective problems. 2007. 2nd ed. Springer, New York.

[6]

Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput. v6 i2. 182-197.

[7]

Knowles, J.D. and Corne, D.W., Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput. v8 i2. 149-172.

[8]

Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength pareto evolutionary algorithm. In: Giannakoglou K, Tsahalis D, Periaux J, Papailou P, Fogarty T, editors. EUROGEN 2001. Evolutionary methods for design, optimization and control with applications to industrial problems, Athens, Greece; 2002. p. 95-100.

[9]

Reyes-Sierra, M. and Coello Coello, C.A., Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res. v2 i3. 287-308.

[10]

Bleuler, S., Laumanns, M., Thiele, L. and Zitzler, E., PISA - a platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (Eds.), Lecture notes in computer science, Springer, Berlin. pp. 494-508.

[11]

Durillo J, Nebro A, Luna F, Dorronsoro B, Alba E. jMetal: a Java framework for developing multi-objective optimization metaheuristics, Tech. Rep. ITI-2006-10, Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, E.T.S.I. Informática, Campus de Teatinos; 2006.

[12]

Sağ, T. and Çunkaş, M., A tool for multiobjective evolutionary algorithms. Adv Eng Softw. v40 i9. 902-912.

[13]

Corne D, Jerram N, Knowles J, Oates M. PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Genetic and evolutionary computation conference (GECCO-2001), Morgan Kaufmann; 2001. p. 283-90.

[14]

Reyes, M. and Coello Coello, C., Improving PSO-based multi-objective optimization using crowding, mutation and ¿-dominance. In: Coello, C., Hernández, A., Zitler, E. (Eds.), LNCS, vol. 3410. Springer. pp. 509-519.

[15]

Nebro, A., Durillo, J., Luna, F., Dorronsoro, B. and Alba, E., Design issues in a multiobjective cellular genetic algorithm. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (Eds.), Lecture notes in computer science, vol. 4403. Springer. pp. 126-140.

[16]

Nebro AJ, Luna F, Alba E, Dorronsoro B, Durillo JJ, Beham A. AbYSS: Adapting Scatter Search to Multiobjective Optimization. IEEE Trans Evol Comput 12(4):439-457.

Digital Library

[17]

Li, H. and Zhang, Q., Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. IEEE Trans Evol Comput. v12 i2. 284-302.

[18]

Greiner, D., Emperador, J., Winter, G. and Galván, B., Improving computational mechanics optimum design using helper objectives: an application in frame bar structures. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (Eds.), Lecture notes in computer science, vol. 4403. Springer, Berlin, Germany. pp. 575-589.

[19]

Durillo, J.J., Nebro, A.J., Luna, F. and Alba, E., Solving three-objective optimization problems using a new hybrid cellular genetic algorithm. In: Rudolph, G., Jensen, T., Lucas, S., Poloni, C., Beume, N. (Eds.), Lecture notes in computer science, vol. 5199. Springer. pp. 661-670.

[20]

Kukkonen S, Lampinen J. GDE3: The third evolution step of generalized differential evolution. In: IEEE congress on evolutionary computation (CEC'2005); 2005. p. 443-50.

[21]

Eskandari, H., Geiger, C.D. and Lamont, G.B., FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (Eds.), Lecture notes in computer science, vol. 4403. Springer. pp. 141-155.

[22]

Zitzler, E. and Künzli, S., Indicator-based selection in multiobjective search. In: Yao, X. (Ed.), Parallel problem solving from nature (PPSN VIII), Springer Verlag, Berlin, Germany. pp. 832-842.

[23]

Nebro, A., Durillo, J., García-Nieto, J., Coello Coello, C., Luna, F. and Alba, E., Smpso: a new pso-based metaheuristic for multi-objective optimization. In: 2009 IEEE symposium on computational intelligence in multicriteria decision-making (MCDM 2009), IEEE Press. pp. 66-73.

[24]

Nebro, A., Alba, E., Molina, G., Chicano, F., Luna, F. and Durillo, J., Optimal antenna placement using a new multi-objective chc algorithm. In: GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, New York (NY, USA). pp. 876-883.

[25]

Beume, N., Naujoks, B. and Emmerich, M., SMS-E MOA: Multiobjective selection based on dominated hypervolume. Eur J Oper Res. v181 i3. 1653-1669.

[26]

Zitzler, E., Deb, K. and Thiele, L., Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput. v8 i2. 173-195.

[27]

Deb, K., Thiele, L., Laumanns, M. and Zitzler, E., Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (Eds.), Evolutionary multiobjective optimization. theoretical advances and applications, Springer, USA. pp. 105-145.

[28]

Huband, S., Hingston, P., Barone, L. and While, L., A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans Evol Comput. v10 i5. 477-506.

[29]

Zhang Q, Zhou A, Zhao SZ, Suganthan PN, Liu W, Tiwari S. Multiobjective optimization test instances for the cec 2009 special session and competition, Tech. Rep. CES-487, University of Essex and Nanyang Technological University, Essex, UK and Singapore, September 2008.

[30]

Kursawe, F., A variant of evolution strategies for vector optimization. In: Schwefel, H., Männer, R. (Eds.), Parallel problem solving for nature, Springer-Verlag, Berlin, Germany. pp. 193-197.

[31]

Fonseca, C. and Flemming, P., Multiobjective optimization and multiple constraint handling with evolutionary algorithms-part ii: application example. IEEE Trans System, Man, Cybern. v28. 38-47.

[32]

Schaffer J. Multiple objective optimization with vector evaluated genetic algorithms. In: Grefensttete J, editor. First international conference on genetic algorithms, Hillsdale, NJ; 1987. p. 93-100.

[33]

Srinivas, N. and Deb, K., Multiobjective function optimization using nondominated sorting genetic algorithms. Evol Comput. v2 i3. 221-248.

[34]

Tanaka M, Watanabe H, Furukawa Y, Tanino T. Ga-based decision support system for multicriteria optimization. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, vol. 2; 1995. p. 1556-1561.

[35]

Osyczka, A. and Kundo, S., A new method to solve generalized multicriteria optimization problems using a simple genetic algorithm. Struct Optimiz. v10. 94-99.

[36]

Kurpati, A., Azarm, S. and Wu, J., Constraint handling improvements for multi-objective genetic algorithms. Struct Multidiscipl Opt. v23 i3. 204-213.

[37]

Ray, T., Tai, K. and Seow, K., An evolutionary algorithm for multiobjective optimization. Eng Opt. v33 i3. 399-424.

[38]

Zitzler, E. and Thiele, L., Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput. v3 i4. 257-271.

[39]

Van Veldhuizen DA, Lamont GB. Multiobjective evolutionary algorithm research: A history and analysis, Tech. Rep. TR-98-03, Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst.Technol., Wright-Patterson, AFB, OH; 1998.

[40]

Knowles J, Thiele L, Zitzler E. A tutorial on the performance assessment of stochastic multiobjective optimizers, Tech. Rep. 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich; 2006.

[41]

Nebro, A.J., Durillo, J.J., Coello Coello, C., Luna, F. and Alba, E., A study of convergence speed in multi-objective metaheuristics. In: Rudolph, G., Jensen, T., Lucas, S., Poloni, C., Beume, N. (Eds.), Lecture notes in computer science, vol. 5199. Springer. pp. 763-772.

[42]

Durillo JJ, Nebro AJ, Coello Coello CA, Luna F, Alba E. A comparative study of the effect of parameter scalability in multi-objective metaheuristics. In: CEC 2008, Hong Kong; 2008. p. 255-66.

[43]

Durillo, J., Nebro, A., Coello, C.C., García-Nieto, J., Luna, F. and Alba, E., A study of multi-objective metaheuristics when solving parameter scalable problems. IEEE Trans Evol Comput. v14 i4. 618-635.

[44]

Durillo, J.J., Nebro, A.J., Luna, F. and Alba, E., On the effect of the steady-state selection scheme in multi-objective genetic algorithms. In: Ehrgott, M., Fonseca, C., Gandibleux, X., Hao, J., Sevaux, M. (Eds.), Lecture notes in computer science, vol. 5467. Springer. pp. 183-197.

[45]

Durillo J, Nebro A, Alba E. The jmetal framework for multi-objective optimization: Design and architecture. In: CEC 2010, Barcelona, Spain; 2010, p. 4138-325.

[46]

Nebro A, Durillo J. jMetal 3.1 User Manual; 2010.

[47]

Zhou A, Jin Y, Zhang Q, Sendhoff B, Tsang E. Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. In: 2006 IEEE Congress on evolutionary computation; 2006. p. 3234-41.

[48]

Hooker, J., Testing heuristics: we have it all wrong. J Heuristics. v1. 33-42.

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Helbig MLi XHandl J(2024)Visualising Found Solutions and Measures for Dynamic Multi-objective OptimisationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664151(2002-2009)Online publication date: 14-Jul-2024
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jMetal: A Java framework for multi-objective optimization
1. Information systems
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cover image Advances in Engineering Software

Advances in Engineering Software Volume 42, Issue 10

October, 2011

176 pages

ISSN:0965-9978

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Copyright © Elsevier Ltd © 2011.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 October 2011

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Cited By

Chen TLi M(2024)Adapting Multi-objectivized Software Configuration TuningProceedings of the ACM on Software Engineering10.1145/36437511:FSE(539-561)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643751
Helbig MLi XHandl J(2024)Visualising Found Solutions and Measures for Dynamic Multi-objective OptimisationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664151(2002-2009)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664151
Ceschia SDa Ros FDi Gaspero LSchaerf ALi XHandl J(2024)EasyLocal++ a 25-year Perspective on Local Search FrameworksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664140(1658-1667)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664140
de Winter RBäck TVan Stein NLi XHandl J(2024)Modular Optimization Framework for Mixed Expensive and Inexpensive Real-World ProblemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664133(1715-1723)Online publication date: 14-Jul-2024
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Li MHan XChu XLiang ZLi XHandl J(2024)Empirical Comparison between MOEAs and Local Search on Multi-Objective Combinatorial Optimisation ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654077(547-556)Online publication date: 14-Jul-2024
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Chen PChen TLi M(2024)MMO: Meta Multi-Objectivization for Software Configuration TuningIEEE Transactions on Software Engineering10.1109/TSE.2024.338891050:6(1478-1504)Online publication date: 15-Apr-2024
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Ida Evangeline SDarwin SPeter Anandkumar PSreenivasan V(2024)Investigating the performance of a surrogate-assisted nutcracker optimization algorithm on multi-objective optimization problemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123044245:COnline publication date: 2-Jul-2024
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