Abstract
Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence approach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
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Project supported by the National Natural Science Foundation of China (Nos. 60074040, 6022506) and the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of China
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Guo, Cx., Hu, Js., Ye, B. et al. Swarm intelligence for mixed-variable design optimization. J. Zhejiang Univ. Sci. A 5, 851–860 (2004). https://doi.org/10.1631/jzus.2004.0851
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DOI: https://doi.org/10.1631/jzus.2004.0851