Abstract
In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, A new Multi-objective optimization algorithm-Modified Multiobjective Brain Storm Optimization (MMBSO) algorithm is proposed. The clustering strategy acts directly in the objective space instead of in the solution space and suggests potential Pareto-dominance areas in the next iteration. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (DBSCAN) clustering and Differential Evolution (DE) mutations are used to improve the performance of MBSO. A group of multi-objective problems with different characteristics were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that MMBSO is a very promising algorithm for solving these tested multi-objective problems.
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Xie, L., Wu, Y. (2014). A Modified Multi-Objective Optimization Based on Brain Storm Optimization Algorithm. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_39
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DOI: https://doi.org/10.1007/978-3-319-11897-0_39
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