Abstract
Artificial bee colony (ABC) has shown strong global search abilities on single-objective problems (SOPs). In order to stretch ABC to tackle many-objective optimization problems (MaOPs), a novel many-objective ABC algorithm on account of decomposition and dimension learning (called MaOABC-DDL) is proposed in this paper. By the decomposition, a MaOP is transformed to several sub-problems, which are simultaneously optimized by an improved ABC algorithm. A novel fitness function is the adoption of the ranking value of each objective. Then, an elite set is constructed according to the fitness value. Built on the elite set, a revised search strategy is designed. In addition, dimension learning is employed to amplify the search capability and Speed up convergence. To verify the performance of MaOABC-DDL, the DTLZ benchmark set is measured in the trials. The outcome shows that the proposed MaOABC-DDL obtains better results than the other five compared algorithms in two performance metrics.
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References
Tanabe, R., Ishibuchi, H.: A review of evolutionary multimodal multiobjective optimization. IEEE Trans. Evol. Comput. 24(1), 193–200 (2020)
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)
Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)
Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Tran. Evol. Comput. 11(6), 712–731 (2007)
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)
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)
Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for realparameter optimization. Inf. Sci. 192, 120–142 (2012)
Karaboga, D., Gorkemli, B.: A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl. Soft Comput. 23(1), 227–238 (2014)
Wang, H., Wang, W.J., Xiao, S.Y., Cui, Z.H., Xu, M.Y., Zhou, X.Y.: Improving ar tifificial Bee colony algorithm using a new neighborhood selection mechanism. Inf. Sci. 527, 227–240 (2020)
Xiao, S., Wang, H., Wang, W., Huang, Z., Zhou, X., Xu, M.: Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation. Appl. Soft Comput. 100, 106955 (2021)
Ye, T.Y., Zeng, T., Zhang, L.Q., Xu, M., Wang, H., Hu, M.: Artificial bee colony algorithm with an adaptive search manner. In: Neural Computing for Advanced Applications, pp. 486–497. Springer Singapore, Singapore (2021). https://doi.org/10.1007/s00521-022-06981-4
Zeng, T., Ye, T., Zhang, L., Xu, M., Wang, H., Hu, M.: Population diversity guided dimension perturbation for artificial bee colony algorithm. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2021. CCIS, vol. 1449, pp. 473–485. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-5188-5_34
Zeng, T., et al.: Artificial bee colony based on adaptive search strategy and random grouping mechanism. Expert Syst. Appl. 192, 116332 (2022)
Ye, T.Y., et al.: Artificial bee colony algorithm with efficient search strategy based on random neighborhood structure. Knowl.-Based Syst. 241, 108306 (2022)
Ye, T.Y., Wang, H., Wang, W.J., Zeng, T., Zhang, L.Q., Huang, Z.K.: Artificial bee colony algorithm with an adaptive search manner and dimension perturbation. Neural Computing and Applications (2022)
Zou, W., Zhu, Y., Chen, H., Zhang, B.: Solving multiobjective optimization problems using artificial bee colony algorithm. Discrete Dyn. Nature Soc. 11(2), 1–37 (2011)
Akbari, R., Hedayatzadeh, R., Ziarati, K., Hassanizadeh, B.: A multi-objective artificial bee colony algorithm. Swarm Evol. Comput. 2, 39–52 (2012)
B. Akay.: Synchronous and asynchronous Pareto-based multi-objective artificial bee colony algorithms. J. Global Opt. 57(2), 415–445 (2013)
Xiang, Y., Zhou, Y., Liu, H.: An elitism based multi-objective artificial bee colony algorithm. Europ. J. Oper. Res. 245(1), 168–193 (2015)
Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)
Yuan, Y., Xu, H., Wang, B., Zhang, B., Yao, X.: Balancing convergence and diversity in decomposition-based many-objective optimizers. IEEE Trans. Evol. Comput. 20(2), 180–198 (2016)
Cheng, R., Jin, Y., Olhofer, M., Sendhoff, B.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016)
Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 62166027), and Jiangxi Provincial Natural Science Foundation (Nos. 20212ACB212004, 20212BAB202023, and 20212BAB202022).
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Wang, S., Wang, H., Wei, Z., Wu, J., Liu, J., Zhang, H. (2022). Many-Objective Artificial Bee Colony Algorithm Based on Decomposition and Dimension Learning. 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_12
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