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An enhanced multi-objective evolutionary optimization algorithm with inverse model. (English) Zbl 1459.90229
Summary: Multi-objective evolutionary algorithm based on the inverse model (IM-MOEA) is a popular method to solve multi-objective optimization problems (MOPs). However, IM-MOEA has some drawbacks such as low accuracy and difficulty in dealing with MOPs with irregular PFs. To address these issues, adaptive reference vector mechanism and nonrandom grouping strategy are employed in IM-MOEA, which enhances the reliability of the inverse model. In addition, a modified selection mechanism is used to choose candidate solutions. Further, an enhanced IM-MOEA with adaptive reference vectors and nonrandom grouping (AN-IMMOEA) is proposed in this paper. The experimental results on 27 MOPs indicate that the proposed method has a better performance than other MOEAs.
90C59 Approximation methods and heuristics in mathematical programming
68W50 Evolutionary algorithms, genetic algorithms (computational aspects)
90C29 Multi-objective and goal programming
Full Text: DOI
[1] Abido, M. A., Multiobjective particle swarm optimization for environmental/economic dispatch problem, Electr. Power Syst. Res., 79, 7, 1105-1113 (2009)
[2] Deng, T.; Lin, C.; Li, Y.; Lu, R., A multi-objective optimization method for energy management control of hybrid electric vehicles using NSGA-II algorithm, J. Xian Jiaotong Univ. (2015)
[3] Nakib, A.; Oulhadj, H.; Siarry, P., Image thresholding based on Pareto multiobjective optimization, Eng. Appl. Artif. Intell., 23, 3, 313-320 (2010)
[4] Sun, J.; Zhang, H.; Zhou, A.; Zhang, Q.; Zhang, K., A new learning-based adaptive multi-objective evolutionary algorithm, Swarm Evol. Comput. (2018)
[5] Zhou, A.; Qu, B. Y.; Li, H.; Zhao, S. Z.; Suganthan, P. N.; Zhang, Q., Multiobjective evolutionary algorithms: a survey of the state of the art., Swarm Evol. Comput., 1, 1, 32-49 (2011)
[6] 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)
[7] Horn, J.; Nafpliotis, N.; Goldberg, D. E., A niched Pareto genetic algorithm for multi-objective optimization, IEEE Conference on Evolutionary Computation IEEE World Congress on Computational Intelligence (1994)
[8] Zitzler, E.; Thiele, L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Trans. Evol. Comput., 3, 4, 257-271 (1999)
[9] Corne, D. W.; Jerram, N. R.; Knowles, J. D.; Oates, M. J., PESA-II: region-based selection in evolutionary multiobjective, Conference on Genetic & Evolutionary Computation (2001)
[10] Zhang, Q.; Hui, L., MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 6, 712-731 (2008)
[11] Liu, H. L.; Gu, F.; Zhang, Q., Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems, IEEE Trans. Evol. Comput., 18, 3, 450-455 (2014)
[12] Beumea, N.; Emmerich, M., SMS-EMOA: multiobjective selection based on dominated hypervolume, Eur. J. Oper. Res., 181, 3, 1653-1669 (2007) · Zbl 1123.90064
[13] Bader, J.; Zitzler, E., HypE: an algorithm for fast hypervolume-based many-objective optimization, Evol. Comput., 19, 1, 45-76 (2011)
[14] Phan, D. H.; Suzuki, J., R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization, Evolutionary Computation (2013)
[15] Solgi, E.; Husseini, S. M.M.; Ahmadi, A.; Gitinavard, H., A hybrid hierarchical soft computing approach for the technology selection problem in brick industry considering environmental competencies: a case study, J. Environ. Manage., 248, 109219 (2019)
[16] Ebrahimnejad, S.; Naeini, M.; Gitinavard, H.; Mousavi, S. M., Selection of it outsourcing services activities considering services cost and risks by designing an interval-valued hesitant fuzzy-decision approach, J. Intell. Fuzzy Syst., 32, 6, 4081-4093 (2017) · Zbl 1376.90031
[17] Gitinavard, H.; Zarandi, M. H.F., A mixed expert evaluation system and dynamic interval-valued hesitant fuzzy selection approach, Int. J. Math.Comput. Phys. Electr. Comput. Eng., 10, 337-345 (2016)
[18] Gitinavard, H.; Akbarpour Shirazi, M., An extended intuitionistic fuzzy modified group complex proportional assessment approach, J. Ind. Syst. Eng., 11, 3, 229-246 (2018)
[19] Gitinavard, H.; Akbarpour Shirazi, M.; Ghodsypour, S. H., A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty, Sci. Iran., 26, 5, 2952-2970 (2019)
[20] Agrawal, R. B.; Deb, K.; Deb, K.; Agrawal, R. B., Simulated binary crossover for continuous search space, Complex Syst., 9, 3, 115-148 (1994) · Zbl 0843.68023
[21] Chen, T.; Ke, T.; Chen, G.; Xin, Y., Analysis of computational time of simple estimation of distribution algorithms, IEEE Trans. Evol. Comput., 14, 1, 1-22 (2010)
[22] Zhang, Q.; Zhou, A.; Jin, Y., RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm, IEEE Trans. Evol. Comput., 12, 1, 41-63 (2008)
[23] Kukkonen, S.; Lampinen, J., An empirical study of control parameters for the third version of generalized differential evolution (GDE3), IEEE International Conference on Evolutionary Computation (2006)
[24] Deb, K.; Sinha, A.; Kukkonen, S., Multi-objective test problems, linkages, and evolutionary methodologies, Genetic & Evolutionary Computation Conference (2006)
[25] Bosman, P. A.N.; Thierens, D., The naive MIDEA: a baseline multi-objective EA, International Conference on Evolutionary Multi-criterion Optimization (2005) · Zbl 1109.68587
[26] Li, Y.; Xia, X.; Li, P.; Jiao, L., Improved RM-MEDA with local learning, Soft Comput., 18, 7, 1383-1397 (2014)
[27] Zhou, A.; Zhang, Q.; Jin, Y., Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Trans. Evol. Comput., 13, 5, 1167-1189 (2009)
[28] Chan, K. P.; Ray, T., An evolutionary algorithm to maintain diversity in the parametric and the objective space, Third International Conference on Computational Intelligence, Robotics and Autonomous Systems(CIRAS 2005) (2005)
[29] Deb, K.; Tiwari, S., Omni-optimizer: a procedure for single and multi-objective optimization, Lect. Notes Comput. Sci., 3410, 47-61 (2005) · Zbl 1109.68592
[30] Cheng, R.; Jin, Y.; Narukawa, K.; Sendhoff, B., A multiobjective evolutionary algorithm using gaussian process based inverse modeling, IEEE Trans. Evol. Comput., 19, 6, 838-856 (2015)
[31] Cheng, R.; Jin, Y.; Narukawa, K., Adaptive reference vector generation for inverse model based evolutionary multiobjective optimization with degenerate and disconnected Pareto fronts, Lect. Notes Comput. Sci., 9018, 127-140 (2015)
[32] Lin, Y.; Han, L.; Jiang, Q., Dynamic reference vectors and biased crossover use for inverse model based evolutionary multi-objective optimization with irregular Pareto fronts, Appl. Intell., 1-27 (2018)
[33] Hornik, K.; Stinchcombe, M.; White, H., Multilayer feedforward networks are universal approximators, Neural Netw., 2, 5, 359-366 (1989) · Zbl 1383.92015
[34] Asafuddoula, M.; Singh, H. K.; Ray, T., An enhanced decomposition-based evolutionary algorithm with adaptive reference vectors, IEEE Trans. Cybern., 48, 8, 1-14 (2018)
[35] Deb, K.; Thiele, L.; Laumanns, M.; Zitzler, E., Scalable multi-objective optimization test problems, Congress on Evolutionary Computation (2002)
[36] Huband, S.; Hingston, P.; Barone, L.; While, R. L., A review of multiobjective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., 10, 5, 477-506 (2006)
[37] Bezerra, L. C.; López-Ibáñez, M.; Stützle, T., An empirical assessment of the properties of inverted generational distance on multi-and many-objective optimization, International Conference on Evolutionary Multi-Criterion Optimization, 31-45 (2017)
[38] Zitzler, E.; Thiele, L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Trans. Evol. Comput., 3, 4, 257-271 (1999)
[39] Ye, T.; Ran, C.; Zhang, X.; Jin, Y., Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum], IEEE Comput Intell Mag, 12, 4, 73-87 (2017)
[40] Cheng, R.; Li, M.; Tian, Y.; Zhang, X.; Yang, S.; Jin, Y.; Yao, X., A benchmark test suite for evolutionary many-objective optimization, Complex Intell. Syst., 3, 1, 67-81 (2017)
[41] Izui, K.; Yamada, T.; Nishiwaki, S.; Tanaka, K., Multiobjective optimization using an aggregative gradient-based method, Struct. Multidiscip. Optim., 51, 1, 173-182 (2015)
[42] Hernández, V. A.S.; Schütze, O.; Wang, H.; Deutz, A.; Emmerich, M., The set-based hypervolume newton method for bi-objective optimization, IEEE Trans. Cybern. (2018)
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