An Evolutionary Algorithm Based Approach to Design Optimization Using Evidence Theory

Abstract

For problems involving uncertainties in design variables and parameters, a biobjective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some interests in classical optimization literature, this is the first attempt to use evidence theory with an EA. Due to EA's flexibility in modifying its operators, nonrequirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidencebased design optimization using an EA is promising. Results on a test problem and two engineering design problems show that the modified evolutionary multiobjective optimization algorithm is capable of finding a widely distributed tradeoff frontier showing different optimal solutions corresponding to different levels of plausibility failure limits. Furthermore, a singleobjective evidencebased EA is found to produce better optimal solutions than a previously reported classical optimization algorithm. Furthermore, the use of a graphical processing unit (GPU) based parallel computing platform demonstrates EA's performance enhancement around 160–700 times in implementing plausibility computations. Handling uncertainties of different types are getting increasingly popular in applied optimization studies and this EA based study is promising to be applied in realworld design optimization problems.