Abstract
With the vast existence of multi-objective optimization problems to the scientific research and engineering applications, Many-objective Evolutionary Algorithms (MaOEAs) demand to systematically perpetuate population diversity and convergence distributions in the objective space with high dimensionality. To fulfill the balance in the relationship between convergence, distributions, and diversity, this paper proposes a directed search many-objective optimization algorithm embodied with kernel clustering strategy (DSMOA-KCS) in decision space where some mechanisms such as adaptive environmental selection which efficiently assimilates design for control of diversity and convergence in the distribution of the solutions in the decision scopes. DSMOA-KCS is a stochastic, multi-start algorithm using clustering to increase efficiency. DSMOA-KCS finds the starting point in the regions of interest. Then, it improves them by the directed search method. DSMOA-KCS is compared with several existing state-of-the-art algorithms (NSGA-III, RSEA, and MOEADPas) on many-objective problems with 5 to 30 objective functions using the Inverted Generational Distance (IGD) performance metric. DSMOA-KCS evaluation results illustrate that it is competitive and promising, performing better with some problems. Then, even distribution, convergence, and diversity are maintained.
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References
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)
Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms. i. unified formulation. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 28(1), 26–37 (1998)
Purshouse, R.C., Fleming, P.J.: An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 133–147. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36970-8_10
Gong, Z., Chen, H., Yuan, B., Yao, X.: Multiobjective learning in the model space for time series classification. IEEE Trans. Cybern. 49(3), 918–932 (2018)
Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: Handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2013)
Jiang, S., Yang, S.: A strength pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization. IEEE Trans. Evol. Comput. 21(3), 329–346 (2017)
Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 48(1), 1–35 (2015)
Li, H., Deng, J., Zhang, Q., Sun, J.: Adaptive epsilon dominance in decomposition-based multiobjective evolutionary algorithm. Swarm Evol. Comput. 45, 52–67 (2019)
Singh, H.K., Bhattacharjee, K.S., Ray, T.: Distance-based subset selection for benchmarking in evolutionary multi/many-objective optimization. IEEE Trans. Evol. Comput. 23(5), 904–912 (2018)
Zhang, Q., Li, H.: Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zhang, X., Tian, Y., Jin, Y.: A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(6), 761–776 (2014)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China under Grants Nos. 62176200, 61773304, and 61871306, the Natural Science Basic Research Program of Shaanxi under Grant No.2022JC-45, 2022JQ-616 and the Open Research Projects of Zhejiang Lab under Grant 2021KG0AB03, the 111 Project, the National Key R &D Program of China, the Guangdong Provincial Key Laboratory under Grant No. 2020B121201001 and the GuangDong Basic and Applied Basic Research Foundation under Grant No. 2021A1515110686.
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Okoth, M.A., Shang, R., Zhang, W., Jiao, L. (2022). A Directed Search Many Objective Optimization Algorithm Embodied with Kernel Clustering Strategy. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_13
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DOI: https://doi.org/10.1007/978-3-031-14903-0_13
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