Abstract
In evolutionary multi-objective optimization, an evolutionary algorithm is invoked to solve an optimization problem involving concurrent optimization of multiple objective functions. Many techniques have been proposed in the literature to solve multi-objective optimization problems including NSGA-II, MOEA/D and MOPSO algorithms. Harmony Search (HS), which is a relatively new heuristic algorithm, has been successfully used in solving multi-objective problems when combined with non-dominated sorting (NSHS) or the breakdown of the multi-objectives into scalar sub-problems (MOHS/D). In this paper, the performance of NSHS and MOHS/D is enhanced by using a previously proposed hybrid framework. In this framework, the diversity of the population is measured every a predetermined number of iterations. Based on the measured diversity, either local search or a diversity enhancement mechanism is invoked. The efficiency of the hybrid framework when adopting HS is investigated using the ZDT, DTLZ and CEC2009 benchmarks. Experimental results confirm the improved performance of the hybrid framework when incorporating HS as the main algorithm.
I. A. Doush—Dr. Iyad Abu Doush, Department Computer Science and Information Systems, American University of Kuwait, Salmiya, Kuwait.
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References
Abraham, A., Jain, L.: Evolutionary multiobjective optimization. Springer (2005)
Abu Doush, I., Bataineh, M.Q.: Hybedrized NSGA-II and MOEA/D with Harmony Search Algorithm to Solve Multi-objective Optimization Problems, pp. 606–614. Springer (2015)
Al-Betar, M.A., Doush, I.A., Khader, A.T., Awadallah, M.A.: Novel selection schemes for harmony search. Appl. Math. Comput. 218(10), 6095–6117 (2012)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the Congress on Evolutionary Computation (CEC-2002), (Honolulu, USA), pp. 825–830 (2002)
Doush, I.A.: Harmony Search with Multi-Parent Crossover for Solving IEEE-CEC2011 Competition Problems, pp. 108–114. Springer, Heidelberg (2012)
Doush, I.A., Al-Betar, M.A., Khader, A.T., Awadallah, M.A., Mohammed, A.B.: Analysis of takeover time and convergence rate for harmony search with novel selection methods. Int. J. Math. Model. Numer. Optim. 4(4), 305–322 (2013)
El-Abd, M.: An improved global-best harmony search algorithm. Appl. Math. Comput. 222, 94–106 (2013)
Esfe, M.H., Hajmohammad, H., Toghraie, D., Rostamian, H., Mahian, O., Wongwises, S.: Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems. Energy 137, 160–171 (2017)
Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Gutjahr, W.J., Pichler, A.: Stochastic multi-objective optimization: a survey on non-scalarizing methods. Ann. Oper. Res. 236(2), 475–499 (2016)
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)
Pavelski, L.M., Almeida, C.P., Gonalves, R.A.: Harmony search for multi-objective optimization. In: 2012 Brazilian Symposium on Neural Networks (SBRN), pp. 220–225. IEEE (2012)
Reyes Sierra, M., Coello Coello, C.A.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)
Ricart, J., Hüttemann, G., Lima, J., Barán, B.: Multiobjective harmony search algorithm proposals. Electron. Notes Theor. Comput. Sci. 281, 51–67 (2011)
Sindhya, K., Miettinen, K., Deb, K.: A hybrid framework for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 17(4), 495–511 (2013)
Zhang, Q., Li, H.: Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester, UK and Nanyang Technological University, Singapore, Special Session on Performance Assessment of Multi-Objective Optimization Algorithms, Technical report (2008)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
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Doush, I.A., Bataineh, M.Q., El-Abd, M. (2019). The Hybrid Framework for Multi-objective Evolutionary Optimization Based on Harmony Search Algorithm. In: Mizera-Pietraszko, J., Pichappan, P., Mohamed, L. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2017. Advances in Intelligent Systems and Computing, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_13
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