In engineering design problems, performance functions evaluate the quality of designs. Among the designs, some of them are classified as good designs if responses from performance functions satisfy a target point or range. An infinite set of good designs in the design space is defined as a solution space of the design problem. In practice, since the performance functions are analytical models or black-box simulations which are computationally expensive, it is difficult to obtain a complete solution space. In this paper, a method that finds a finite set of good designs, which is included in a solution space, is proposed. The method formulates the problem as optimization problems and utilizes gray wolf optimizer (GWO) in the way of design exploration. Target points of the exploration process are defined by clustering intermediate solutions for every iteration. The method is tested with a simple two-dimensional problem and an automotive vehicle design problem to validate and check the quality of solution points.
Multiple Target Exploration Approach for Design Exploration Using a Swarm Intelligence and Clustering
Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received April 8, 2018; final manuscript received March 8, 2019; published online April 22, 2019. Assoc. Editor: Mian Li.
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Han, H., Chang, S., and Kim, H. (April 22, 2019). "Multiple Target Exploration Approach for Design Exploration Using a Swarm Intelligence and Clustering." ASME. J. Mech. Des. September 2019; 141(9): 091401. https://doi.org/10.1115/1.4043201
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