Abstract
When solving multi-objective problems, traditional methods face increased complexity and convergence difficulties because of the increasing number of objectives. This paper proposes a high-dimensional multi-objective particle swarm algorithm that utilizes radial projection to reduce the dimensionality of high-dimensional particles. Firstly, the solution vector space coordinates undergo normalization. Subsequently, the high-dimensional solution space is projected onto 2-dimensional radial space, aiming to reduce computational complexity. Following this, grid partitioning is employed to enhance the efficiency and effectiveness of optimization algorithms. Lastly, the iterative solution is achieved by utilizing the particle swarm optimization algorithm. In the process of iteratively updating particle solutions, the offspring reuse-based parents selection strategy and the maximum fitness-based elimination selection strategy are used to strengthen the diversity of the population, thereby enhancing the search ability of the particles. The computational expense is significantly diminished by projecting the solution onto 2-dimensional radial space that exhibits comparable characteristics to the high-dimensional solution, while simultaneously maintaining the distribution and crowding conditions of the complete point set. In addition, the offspring reuse-based parents selection strategy is used to update the external archive set, further avoiding premature convergence to local optimal solution. The experimental results verify the effectiveness of the method in this paper. Compared with four state-of-the-art algorithms, the algorithm proposed in this paper has high search efficiency and fast convergence in solving high-dimensional multi-objective optimization problems, and can also obtain higher quality solutions.
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References
Russell, E., James, K.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Qiuzhen, L.: Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems. IEEE Trans. Evolution. Comput. 22(1), 32–46 (2018)
Qingfu, Z., Hui, L.: MOEA/D: a multi objective evolutionary algorithm based on decomposition. IEEE Trans. Evolution. Comput. 11(6), 712–731 (2007)
Yuan, Y., Hua, X., Bo, D.: An improved NSGA-III procedure for evolutionary many-objective optimization. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (2014)
Zhang, Z.: A many-objective optimization based intelligent intrusion detection algorithm for enhancing security of vehicular networks in 6G. IEEE Trans. Veh. Technol. 70(6), 5234–5243 (2021)
Mohamad, Z., Mohd Z.: A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Appl. Soft Comput. 70, 680–700 (2018)
Tianyou, C., Weijian, K., Jinliang, D.: Review of high-dimensional multi-objective evolutionary algorithms. Control Decision 4(3), 6 (2010)
Castellanos-Garzón, J.A., Armando GarcÃa, C.: A visual analytics framework for cluster analysis of DNA microarray data. In: Expert Systems with Applications, pp.758–774 (2013)
David, J., Walker, R.M., Jonathan, E.: Visualizing mutually non-dominating solution sets in many-objective optimization. IEEE Trans. Evolution. Comput. 17(2), 165–184 (2013)
Ibrahim, A.: 3D-RadVis: visualization of Pareto front in many-objective optimization. In: Evolutionary Computation (2016)
Cheng, H.: A radial space division based evolutionary algorithm for many-objective optimization. Appl. Soft Comput. 61, 603–621 (2017)
Qinmu, P.: Retinal vessel segmentation based on radial projection and semi-supervised learning. Ph.D. thesis, Huazhong University of Science and Technology (2011)
EngAik, L., WeiHong, T., KadriJunoh, A.: An improved radial basis function networks based on quantum evolutionary algorithm for training nonlinear datasets. IAES Int. J. Artif. Intell. 120–131 (2019)
Pingan, D.: Basic principles of finite element meshing. Mech. Des. Manuf. 4, 34–36 (2000)
Pujia, W.: Research on Dimensionality Reduction Algorithm for scRNAseq Data Based on Generative Adversarial Networks and Autoencoders, p. 1 (2021)
Yuan, L.: Research on Environmental Selection Strategies for High-Dimensional Multi Objective Optimization Algorithms, p. 1 (2017)
Minqiang, L.: The fundamental theory and application of genetic algorithm. Artif. Intell. Robot. Res. (2002)
Ishibuchi, H.: Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes. IEEE Trans. Evolution. Comput. 21(2), 169–190 (2017)
Shanbhag, G.V.: “Mesoporous sodalite: a novel, stable solid catalyst for base-catalyzed organic transformations. J. Catal. 264(1), 88–92 (2009)
Mifa, K.: SPEA2+: improving the performance of the strength Pareto evolutionary algorithm 2. In: Xin, Y. (ed.) Parallel Problem Solving from Nature - PPSN VIII, pp. 742–751. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_75
Ying, Z., Rennong, Z., Jialiang, Z.: Improving decompostion based evolutionary algorithm for solving dynamic firepower allocation multi-objective optimization model. Acta Armament. 36,1533–1540 (2015)
Xiaopeng, W.: Pareto genetic algorithm in multi-objective optimization design. J. Syst. Eng. Electron. 25(12), 4 (2003)
Yanan, S., Gary, G.Y., Zhang, Y.: IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans. Evolution. Comput. 23(2), 173–187 (2019)
Hub, S., Hingston, P.: An evolution strategy with probabilistic mutation for multi-objective optimisation. In: The 2003 Congress on Evolutionary Computation, 2003 (CEC 2003) (2004)
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The work was supported by Science and Technology Project of Jiangxi Provincial Department of Education under grant GJJ190958.
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Tan, D., Zhou, R., Liu, X., Lu, M., Fu, X., Li, Z. (2024). High-Dimensional Multi-objective PSO Based on Radial Projection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_18
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