Abstract
Multi-Objective Evolutionary Algorithms (MOEAs) have been successful in solving mathematical and real-world multi-objective optimization problems by evolving a set of optimal solutions, which are known as Pareto-optimal solutions. However, there are certain limitations with those algorithms such as slow convergence, lack of effective terminating condition to name a few. To address such challenges, hybrid MOEAs are being designed and studied where the global exploration power of MOEAs are combined with local exploitation modules of various numerical optimization techniques. However, hybridization itself brings new challenges in its implementation. In this work, a hybrid MOEA is presented in which random mutations are performed on the initial population to start with a better and diverse set of solutions. Moreover, a local search module is coupled to execute periodically on a least crowded non-dominated solution at a certain interval of generations. The proposed algorithm is tested on a set of benchmark multi-objective optimization problems and compared with the NSGA-II. The convergence plots demonstrate the superiority of the proposed algorithm over NSGA-II.
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
References
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, 1st edn. Wiley, New York (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., et al. (eds) Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100. International Center for Numerical Methods in Engineering (CIMNE) (2002)
Sharma, D., Basha, S.Z., Kumar, S.A.: Diversity over dominance approach for many-objective optimization on reference-points-based framework. In: Deb et al. (eds) 10th International Conference Proceedings on Evolutionary Multi-Criterion Optimization. Michigan State University, East Lansing, USA, (2019) (to appear)
Agarwal, D., Sharma D.: Experimental study on bound handling techniques for multi-objective particle swarm optimization. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications, vol 424, pp. 555–564. Advances in Intelligent Systems and Computing, Cham (2016)
Sharma, D., Collet, P.: An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization. In: Proceedings of The 12th Annual Conference on Genetic and Evolutionary Computation (GECCO’10), pp. 479–486. ACM, New York (2010)
Sindhya, K., Deb, K., Miettinen, K.: A local search based evolutionary multi-objective optimization approach for fast and accurate convergence. In: International Conference on Parallel Problem Solving from Nature, pp. 815–824 (2008)
Adrian, T.A., Hopgood, E.-M., Nolle, L., Battersby, A.: Hybrid genetic algorithms: a review. Eng. Lett. 13(2), 124137 (2006)
Sindhya, K., Miettinen, K., Deb, K.: A hybrid framework for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 17(4), 495–511 (2013)
Ichibuchi, H., Narukawa, K.: Some issues on the implementation of local search in evolutionary multi-objective optimization. In: Genetic and Evolutionary Computation Conference, pp. 1246–1258 (2004)
Ishibuchi, H., Hitotsuyanagi, Y., Wakamatsu, Y., Nojima, Y.: How to choose solutions for local search in multi-objective combinatorial memetic algorithms. In: International Conference on Parallel Problem Solving from Nature, pp. 516–525 (2010)
Sharma, D., Kumar, A., Deb, K., Sindhya, K.: Hybridization of SBX based NSGA-II and sequential quadratic programming for solving multi-objective optimization problems. In: The proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 3003–3010, Singapore (2007)
Kumar, A., Sharma, D., Deb, K.: A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming. In: The Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 3011–3018, Singapore (2007)
Sindhya, K., Deb, K., Miettinen, K.: Improving convergence of evolutionary multi-objective optimization with local search: a concurrent hybrid algorithm. Nat. Comput. 10(4), 1407–1430 (2011)
Sharma, D., Deb, K., Kishore, N.N.: Domain-Specific initial population strategy for compliant mechanisms using customized genetic algorithm. Struct. Multi. Optim. 43(4), 541–554 (2011)
Deb, K.: Optimization for Engineering Design: Algorithms and Examples, 2nd edn. PHI Learning Pvt. Ltd., New Delhi, India (2012)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. J. 8(2), 125–148 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saikia, R., Sharma, D. (2019). Hybridizing Evolutionary Multi-objective Algorithm Using Random Mutations and Local Searches. In: Narayanan, R., Joshi, S., Dixit, U. (eds) Advances in Computational Methods in Manufacturing. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-32-9072-3_75
Download citation
DOI: https://doi.org/10.1007/978-981-32-9072-3_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9071-6
Online ISBN: 978-981-32-9072-3
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)