Abstract
Using swarm intelligence to obtain multiple optima in a single run simultaneously proved efficient for solving multimodal optimization problems (MOPs). However, the existing studies fail to resolve the contradiction between the required solution accuracy and the number of solutions. In this paper, an improved brain storm optimization (BSO) algorithm based on knowledge learning (KLBSO) is proposed as a solution to the problem. The properties of the improved algorithm and the domain knowledge of the problem are combined during the search process. Two factors need to be taken into account to solve a MOP: the accuracy and the diversity of the solution set. In the proposed algorithm, there are two learning approaches. Firstly, improving the learning method by replacing the perturbation operator of the random solution with the inter-solution learning of the worst solutions, improves the optimization ability of the algorithm. Secondly, by analyzing the MOPs, adding an archive set guarantees the solution’s diversity. To assess the efficiency of KLBSO, eight benchmark functions with various sizes and complexities were used. Comparing the results of KLBSO with those of state-of-the-art methods which are brain storm optimization algorithm (BSO), brain storm optimization algorithm in objective space (BSOOS), two kinds of pigeon-inspired optimization algorithms (PIO, PIOr), the comparison results show that the KLBSO is able to solve the contradiction between required solution accuracy and the number of solutions, and improves the outcomes where BSO is ranked first followed by the test algorithms.
This work is partially supported by National Natural Science Foundation of China (Grant No. 61806119), Fundamental Research Funds for the Central Universities (No. GK202201014), and Graduate innovation team project of Shaanxi Normal University (No. TD2020014Z).
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References
Cheng, S., Lei, X., Lu, H., Zhang, Y., Shi, Y.: Generalized pigeon-inspired optimization algorithms. Science China Inf. Sci. 62(7), 1–3 (2019). https://doi.org/10.1007/s11432-018-9727-y
Cheng, S., Ma, L., Lu, H., Lei, X., Shi, Y.: Evolutionary computation for solving search-based data analytics problems. Artif. Intell. Rev. 54(2), 1321–1348 (2020). https://doi.org/10.1007/s10462-020-09882-x
Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. 46(4), 445–458 (2016). https://doi.org/10.1007/s10462-016-9471-0
Cheng, S., Zhang, M., Shi, Y., Lu, H., Lei, X., Wang, R.: Generalized pigeon-inspired optimization algorithm for balancing exploration and exploitation. SCIENTIA SINICA Technologica (2022). https://doi.org/10.1360/SST-2021-0371
Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7(1), 24–37 (2014). https://doi.org/10.1108/IJICC-02-2014-0005
Epitropakis, M.G., Burke, E.K.: Hyper-Heuristics, pp. 1–57. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-07153-4_32-1
Eppe, M., Gumbsch, C., Kerzel, M., Nguyen, P.D.H., Butz, M.V., Wermter, S.: Intelligent problem-solving as integrated hierarchical reinforcement learning. Nat. Mach. Intell. 4, 11–20 (2022). https://doi.org/10.1038/s42256-021-00433-9
Hua, Y., Liu, Q., Hao, K., Jin, Y.: A survey of evolutionary algorithms for multi-objective optimization problems with irregular pareto fronts. IEEE/CAA J. Autom. Sinica 8, 303 (2021). https://doi.org/10.1109/JAS.2021.1003817
Kennedy, J., Eberhart, R., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publisher, San Francisco (2001)
Li, X.: Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans. Evol. Comput. 14(1), 150–169 (2010). https://doi.org/10.1109/TEVC.2009.2026270
Li, X., Engelbrecht, A., Epitropakis, M.G.: Benchmark functions for CEC 2013 special session and competition on niching methods for multimodal function optimization. Evolutionary Computation and Machine Learning Group, RMIT University, Australia, Technical report (2013)
Lu, H., Shi, J., Fei, Z., Zhou, Q., Mao, K.: Analysis of the similarities and differences of job-based scheduling problems. Eur. J. Oper. Res. 270(3), 809–825 (2018). https://doi.org/10.1016/j.ejor.2018.01.051
Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21515-5_36
Shi, Y.: An optimization algorithm based on brainstorming process. Int. J. Swarm Intell. Res. (IJSIR) 2(4), 35–62 (2011). https://doi.org/10.4018/jsir.2011100103
Shi, Y.: Brain storm optimization algorithm in objective space. In: Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015), pp. 1227–1234. IEEE, Sendai, Japan (2015). https://doi.org/10.1109/CEC.2015.7257029
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Wang, X., Liu, Y., Cheng, S. (2022). Knowledge Learning-Based Brain Storm Optimization Algorithm for Multimodal Optimization. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_11
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