An EA-based approach to design optimization using evidence theory
Abstract
For problems involving uncertainties in design variables and parameters, a bi-objective 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, such a consideration in EA is rare. Due to EA’s flexibility in its operators, non-requirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidence-based design optimization using an EA is promising. Results on a test problem and a couple of engineering design prob-lems show that the modified evolutionary multi-objective optimization (EMO) algorithm is capable of finding a widely distributed trade-off frontier showing different optimal solu-tions corresponding to different levels of plausibility failure limits. Furthermore, a single-objective evidence based EA is found to produce better optimal solutions than a previously reported classical optimization procedure. Handling uncer-tainties of different types are getting increasingly popular in applied optimization studies and more such studies using EAs will make EAs more useful and pragmatic in practical optimization problem-solving tasks. Categories and Subject Descriptors J.6 [Computer Applications]: Computer-aided engineer-ing—computer-aided design
Authors: Rupesh Kumar Srivastava, Kalyanmoy Deb
Published in: Genetic and Evolutionary Computation Conference (GECCO) (2011)