Abstract
Most of refining processes were optimized using single objective approach, but practically such complex processes must be optimized with several objectives. Inspired by the theory of three-way decisions, a multi-objective optimization algorithm based on preference three-way decomposition is proposed in this paper. First, according to the preferences of the DM, the analytic hierarchy process (AHP) is used to sort objectives. Then, based on the idea of three-way decisions, these objectives are divided into three sub-parts as the primary objective set, the secondary objective set and the general objective set. Besides, a multi-group parallel optimization algorithm is presented to solve each sub-optimization problem. Finally, based on Non-dominated set of the three sub-problems, a set of external preservation sets are formed so as to get the optimal set that the DM is interested in. Experimental results show that the proposed method can reduce the workload of the DM and obtain more accurately converge to the optimal frontiers of the optimization problems.
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References
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London (2005). https://doi.org/10.1007/1-84628-137-7_6
Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Conference on Genetic and Evolutionary Computation, pp. 635–642. ACM (2006)
Gong, D.W., Liu, Y.P., Sun, X.Y., et al.: Parallel many-objective evolutionary optimization using objectives decomposition. Acta Autom. Sin. 41(8), 1438–1451 (2015)
Kaddani, S., Vanderpooten, D., Vanpeperstraete, J.M., et al.: Weighted sum model with partial preference information: PineGreen application to multi-objective optimization. Eur. J. Oper. Res. 260(2), 665–679 (2017)
Narzisi, G.: Multi-objective optimization: a quick introduction. New York University lectures (2008)
Purshouse, R.C., Fleming, P.J.: Evolutionary many-objective optimisation: an exploratory analysis. Evol. Comput. 3, 2066–2073 (2003). CEC
Shen, X., Yu, G., Chen, Q., et al.: A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic. Comput. Optim. Appl. 46(1), 159–188 (2010)
Yao, Y.Y.: The superiority of three-way decisions in probabilistic rough set models. Inf. Sci. 181(6), 1080–1096 (2011)
Zeman, J.: Ontological and gnoseological aspects of contradiction and their importance in analysis of the development of scientific knowledge. In: Handbook of Professional Ethics for Psychologists (1984)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61751312, 61533020 and 61379114.
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Fu, Z., Yu, H., Zhang, H., Chen, X. (2018). A Multi-objective Optimization Algorithm Based on Preference Three-Way Decomposition. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_1
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DOI: https://doi.org/10.1007/978-3-319-99247-1_1
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