Abstract
Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. Therefore, an efficient tool is required which can handle multiple objectives simultaneously in order to provide a set of desired solutions. In view of this, multi-objective optimization (MOO) attracts the attention of the researchers since last few decades. Many classical optimization techniques have been proposed by the researchers to solve the multi-objective optimization problems. However mostly, the gradient-based approaches fail to handle complex MOO problems. Hence, as an alternative, researchers have shown their interest toward population-based optimization approaches to solve the MOO problems and come up with convincing results even in the complex environment. Evolutionary algorithms (EAs), which are the first in the group of population-based approach, enjoy almost a decade in providing the solutions to MOO problems. The real challenge is to achieve the set of solutions called Pareto-optimal set. The smooth landing on such set is only possible if there exists diversified solution in the population. Due to the continuous effort, there is a gradual development in the proposition of various efficient Pareto-based approaches in the literature to solve MOEAs. A critical review of those approaches is being carried out in this present study.
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References
Guliashki, V., Toshev, H., Korsemov, C.: Survey of evolutionary algorithms used in multiobjective optimization. Prob. Eng. Cybernet. Robot. 42–54 (2009)
Elarbi, M., Bechikh, S., Ben Said, L., Datta, R.: Multi-objective optimization: classical and evolutionary approaches. In: Bechikh, S., Datta, R., Gupta, A. (eds.) Recent Advances in Evolutionary Multi-objective Optimization. Adaptation, Learning, and Optimization, p. 20. Springer, Cham (2017)
Schder, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms, Unpublished doctoral dissertation, Vanderbilt University (1984)
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette, J. (ed.), Proceedings of an International Conference on Genetic Algorithms and their Applications, pp. 93–100 (1985)
Horn. J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, pp. 67–72. IEEE Service Centre, Piscataway, NJ (1994)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)
Srinivas, N., Deb, K.: Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol. Comput. 2, 221–248 (1994)
Knowles, J.D., Corne, D.W.: The Pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimization. In: CEC’99: Proceedings of the 1999 Congress on Evolutionary Computation, IEEE Service Center, Piscataway, New Jersey (1999)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3, 257–271 (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Comput. Eng. Netw. Lab. (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, Tech. Rep. 103 (2001)
Kim, M., Hiroyasu, T., Miki, M., Watanabe, S.: SPEA2+ : improving the performance of the strength pareto evolutionary algorithm 2 parallel problem solving from nature—PPSN VIII, pp. 742–751. Springer (2004)
Corne, D.W., Knowles, J.D., Oates, M.J.: The Pareto envelope-based selection algorithm for multiobjective optimization. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN VI. LNCS. Springer, Heidelberg, 1917, pp. 839–848 (2000)
Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001). Morgan Kaufmann, San Francisco, 283–290 (2001)
Li, M., Yang, S., Liu, X., Wang, K.: IPESA-II: improved pareto envelope-based selection algorithm II. In: Purshouse R.C., Fleming P.J., Fonseca C.M., Greco S., Shaw J. (eds.) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol. 7811. Springer, Berlin, Heidelberg (2013)
Deb, K., Mohan, M., Mishra, S.: Evaluating the ε-dominated based multi-objective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evol. Comput. 13(4), 501–525 (2005)
Tiwari, S., Koch, P., Fadel, G., Deb, K.: AMGA: An archive-based micro genetic algorithm for multi-objective optimization. In: Genetic and Evolutionary Computation Conference (GECCO 2008), pp. 729–736. ACM (2008)
Tiwari, S., Fadel, G., Deb, K.: AMGA2: improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization. Eng. Opt. 371–401, Taylor and Francis (2011)
Nag, K., Pal, T., Pal, N.: ASMiGA: an archive-based steady state micro genetic algorithm. IEEE Trans. Cybern. 45(1), 40–52 (2015)
Praditwong, K., Yao, X.: A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm. In: International Conference on Computational Intelligence and Security, pp. 286–291. IEEE Press (2006)
Lukasiewycz, M.: Opt4J: a modular framework for metaheuristic optimization. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. ACM (2011)
YARPIZ: http://yarpiz.com/category/multiobjective-optimization
Tian, Y., Cheng, R., Zhang, X., Jin, Y.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization. IEEE Comput. Intelligen. Mag.
PYTHON https://pypi.python.org/pypi
KANGAL LAB: http://www.iitk.ac.in/kangal/codes.shtml
Horn, J., Nafpliotis, N.: Multiobjective Optimisation Using The Niched Pareto Genetic Algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Champaign (1994)
Hajela, P., Lin, C.Y.: Genetic search strategies in multicriterion optimal design. In: Structural Optimization, New York, Springer, vol. 4, pp. 99–107 (1992)
Eskandari, H., Geiger, C.D., Lamont, G.B.: Fastpga a dynamic population sizing approach for solving expensive multiobjective optimization problems. In: Evolutionary Multiobjective Optimization Conference on EMO, pp. 141–155 (2007)
Kukkonnen, S., Lampinen, J.: GDE3: the third evolution step of generalized differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 443–450 (2005)
Karahan, I., Köksalan, M. (2010). A territory defining multiobjective evolutionary algorithm and preference incorporation. IEEE Trans. Evol. Comput. 14(4), 636–664
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets MOEA/D and NSGA-II. IEEE Trans. Evolution. Comput. 12(2), 284–302 (2009)
Nebro, A.J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J.J., Beham, A.: AbYSS: adapting scatter search to multiobjective optimization. IEEE Tans. Evol. Comput. 12(4), 439–453 (2008)
Zhang, Q., Li, Hui: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guerv´os, J.J., Bullinaria, J.A., Rowe, J.E., Tiˇno, P., Kab´an, A., Schwefel, H.-P. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evolution. Comput. 18(4), 577–601 (2014)
Emmerich, M., Beume, N., Naujoks, B.: An EMO algorithm using the hypervolume measure as selection criterion. European J. Operation. Res. 3, 1653–16 (2007)
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Dutta, S., Das, K.N. (2019). A Survey on Pareto-Based EAs to Solve Multi-objective Optimization Problems. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_64
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