Abstract
Many-objective optimization problems (MaOPs) refer to multi-objective optimization problems (MOPs) containing large number of objectives, typically more than four. The complexity of MOPs grows rapidly in size with the number of objectives, making the problem quickly intractable. Though various many-objective evolutionary algorithms (MaOEAs) have been proposed to solve MaOPs, it is difficult to balance the convergence and population diversity. In order to address the above issues, this paper proposes a many-objective evolutionary algorithm based on dominance and objective space decomposition (called DDMaOEA), in which fast non-dominated sorting cooperates with objective space decomposition to maintain the population diversity and convergence during the environment selection. To verify the performance of DDMaOEA, 13 benchmark problems with 3, 5, 8, and 10 objectives are used. Experimental results show that DDMaOEA achieves better performance when compared with five other Many-objective optimization algorithms.
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References
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)
Wanger, A., Mills, K., Nelson, P.W., Rex, J.H.: Comparison of Etest and national committee for clinical laboratory standards broth macrodilution method for antifungal susceptibility testing: enhanced ability to detect amphotericin B-resistant Candida isolates. Antimicrob Agents Chemother. 39(11), 2520–2522 (1995)
Zhan, Z.H., Li, J.J., Cao, J.N., Zhang, J., Chung, H.H., Shi, Y.H.: Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems. IEEE Trans. Cybern. 43(2), 445–463 (2013)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
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)
Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2014)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. In: IEEE Transactions on Evolutionary Computation, 13(2), 284–302 (2009)
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 203–208 (2009)
Chen, B., Zeng, W., Lin, Y.: A new local search-based multiobjective optimization algorithm. IEEE Trans. Evol. Comput. 19(1), 50–73 (2015)
Wq, A., Jz, A., Gw, B.: Evolutionary many-Objective algorithm based on fractional dominance relation and improved objective space decomposition strategy - ScienceDirect. Swarm and Evolutionary Computation. 60 (2020)
Reed, P.M., Hadka, D., Herman, J.D.: Evolutionary multi-objective optimization in water resources: The past, present, and future. In: Advances in Water Resources. 51(JAN.), 438–456 (2013)
Liu, H., 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)
Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Systems. 9(3), 193–212 (1995)
Zeng, T., et al.: Artificial bee colony based on adaptive search strategy and random grouping mechanism. Expert Syst. Appl. 192, 116332 (2022)
Lin, Q., Li, J., Du, Z., Chen, J., Ming, Z.: A novel multi-objective particle swarm optimization with multiple search strategies. Europ. J. Oper. Res. 247(3), 732–744 (2015)
Dai, C., Wang, Y., Ye, M.: A new multi-objective particle swarm optimization algorithm based on decomposition. Inf. Sci. 325, 541–557 (2015)
Pan, L., Xu, W., Li, L., He, C., Cheng, R.: Adaptive simulated binary crossover for rotated multi-objective optimization. Swarm Evol. Comput. 60, 100759 (2021)
Coello, C.A., Lechuga, M.S.: MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation. 1051–1056 (2002)
Tian, Y., Cheng, R., Zhang, X.Y., Jin, Y.C.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)
Pierro, F.D., Khu, S.T., Savic, D.A.: An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Trans. Evol. Comput. 11(1), 17–45 (2007)
Karafotias, G., Hoogendoorn, M., Eiben, A.E.: Parameter Control Evol. Algorithms. Evol. Comput. IEEE Trans. 19(2), 167–187 (2007)
Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 62166027), and Jiangxi Provincial Natural Science Foundation (Nos. 20212ACB21 2004, 20212BAB202023, and 20212BAB202022).
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Wei, Z. et al. (2022). Many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_16
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DOI: https://doi.org/10.1007/978-981-19-6135-9_16
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