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PubMed · 15495314

Swarm intelligence for mixed-variable design optimization.

Chuang-Xin Guo·Jia-Sheng Hu·Bin Ye·Yi-Jia Cao
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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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AlgorithmsAnimalsArtificial IntelligenceBehavior, AnimalBiomimeticsComputer SimulationComputer-Aided DesignEquipment DesignFeedbackModels, TheoreticalSocial BehaviorComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Chuang-Xin Guo, Jia-Sheng Hu, Bin Ye, Yi-Jia Cao. 2004. Swarm intelligence for mixed-variable design optimization.. https://doi.org/10.1631/jzus.2004.0851

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