Abstract
Multi-objective problems (MOP) are an important type of real-world problem that involves more than one objective that must be optimized simultaneously. There exists a wide variety of metaheuristic algorithms aimed to work on that type of problems, however, the selection of which algorithm should be used to a given MOP depends on the expertise of the researcher. This decision is not straightforward and usually means to use extra computational resources to try different multi-objective metaheuristics even before to try to solve the interested domain. In the state of the art, there exists several metrics to compare and contrasts the performance of two or more given multi-objective algorithms. In this work, we use these metrics to compare a set of well-known multi-objective metaheuristics over the continuous CEC 2009 benchmark with the objective to give the interested researcher useful information to properly select a multi-objective algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wu, J., Azarm, S.: Metrics for quality assessment of a multiobjective design optimization solution set. J. Mech. Des. 123(1), 18 (2001)
Giagkiozis, I., Purshouse, R.C., Fleming, P.J.: An overview of population-based algorithms for multi-objective optimisation. Int. J. Syst. Sci. pbo˙ijss˙final Int. J. Syst. Sci. 46(00), 1–42 (2013)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 181–197 (2002)
Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
Dorado, D., Guasmayán, F., Bravo, M., Peluffo, D.: Estudio comparativo de NSGA-II y PSO como métodos de optimización multiobjetivo en problemas con frente optimo de pareto convexo. Nov 2016
Coello Coello, C.A., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, Vol. 2, pp. 1051–1056 (2002)
Sierra, M.R., Coello Coello, C.A.: Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance, pp. 505–519. Springer, Berlin, Heidelberg (2005)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 203–208 (2009)
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/ D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Khan, W., Zhang, Q.: MOEA/D-DRA with two crossover operators. In 2010 UK Workshop on Computational Intelligence, UKCI 2010, pp. 1–6 (2010)
Riquelme, N., Von Lücken, C., Barán, B.: Performance metrics in multi-objective optimization. In: Proceedings—2015 41st Latin American Computing Conference, CLEI 2015, pp. 1–11 (2015)
Lwin, K., Qu, R., Kendall, G.: A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Appl. Soft Comput. J. 24, 757–772 (2014)
Reguianski, T.L.: The air force institute of technology. IRE Trans. Educ. E-5(2), 117–118 (1962)
Coello Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)
Li, X., Wei, L.X., Fan, R., Sun, H., Hu, Z.Y.: A hybrid multiobjective particle swarm optimization algorithm based on R2 indicator. IEEE Access 6, 14710–14721 (2018)
Liu, H.L., Chen, L., Zhang, Q., Deb, K.: Adaptively allocating search effort in challenging many-objective optimization problems. IEEE Trans. Evol. Comput. 22(3), 433–448 (2018)
Sato, H., Aguirre, H.E., Tanaka, K.: Local dominance using polar coordinates to enhance multiobjective evolutionary algorithms. In: Proceedings 2004 Congress Evolutionary Computation, vol. 1, pp. 188–195 (2004)
Dawson, P., Parks, G., Jaeggi, D., Molina-Cristobal, A., Clarkson, P.J.: The development of a multi-threaded multi-objective tabu search algorithm. In: Dawson, P., Parks, G., Jaeggi, D., Molina-Cristobal, A., John Clarkson, P (eds.) Evolutionary Multi-Criterion Optimization. Springer, Berlin, Heidelberg, pp. 242–256 (2007)
Zitzler, E., Knowles, J., Thiele, L.: Quality Assessment of Pareto Set Approximations, pp. 373–404. Springer, Berlin, Heidelberg (2008)
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W.: Multiobjective optimization test instances for the CEC 2009 special session and competition. In: 2009 IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1–30 (2009)
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 203–208 (2009)
Soria-Alcaraz, J.A., Sotelo-Figueroa, M.A., Espinal, A.: Statistical comparative between selection rules for adaptive operator selection in vehicle routing and multi-knapsack problems. In: Studies in Computational Intelligence, Vol. 749, pp. 389–400 (2018)
Jia, K., He, Z.: DOA identification of communication emitters based on Shapiro-Wilk test and divisive hierarchical cluster analysis Kexin. In: 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1–4 (2010)
Danila, A., Ungureanu, D., Moraru, S.A., Voicescu, N.: An implementation of the variance analysis (ANOVA) for the power factor optimization at distribution level in smart grid. In: Proceedings—2017 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017, pp. 48–53 (2017)
Soria-Alcaraz, J.A., Espinal, A., Sotelo-Figueroa, M.A.: Evolvability metric estimation by a parallel perceptron for on-line selection hyper-heuristics. IEEE Access 5, 7055–7063 (2017)
Durillo, J.J., Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E.: jMetal: A Java Framework for Developing Multi-Objective Optimization Metaheuristics (2015)
Durillo, J.J., Nebro, A.J., Alba, E.: The jMetal framework for multi-objective optimization: design and architecture. In IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Acknowledgements
Authors express their gratitude to the Universidad de Guanajuato (UG) for its support provided to carry out the present research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Juarez-Santini, C., Soria-Alcaraz, J.A., Sotelo-Figueroa, M.A., Velino, EJ. (2020). Comparative Analysis of Multi-objective Metaheuristic Algorithms by Means of Performance Metrics to Continuous Problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-35445-9_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35444-2
Online ISBN: 978-3-030-35445-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)