Portal de Periódicos da CAPES

Multi-Objective Optimization Using an Evolutionary Algorithm Embedded with Multiple Spatially Distributed Surrogates

2017; World Scientific; Linguagem: Inglês

10.1142/9789813148239_0005

2425-0198

Kalyan Shankar Bhattacharjee, Amitay Isaacs, Tapabrata Ray,

Advanced Multi-Objective Optimization Algorithms

Advances in Process Systems EngineeringMulti-Objective Optimization, pp. 135-155 (2017) No AccessChapter 5: Multi-Objective Optimization Using an Evolutionary Algorithm Embedded with Multiple Spatially Distributed SurrogatesKalyan Shankar Bhattacharjee, Amitay Isaacs, and Tapabrata RayKalyan Shankar BhattacharjeeSchool of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australia, Amitay IsaacsSchool of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australia, and Tapabrata RaySchool of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australiahttps://doi.org/10.1142/9789813148239_0005Cited by:3 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: For most practical optimization problems involving computationally expensive analysis, a brute force approach relying on actual analysis is not computationally viable. Surrogates and approximations are regularly used in lieu of computationally expensive analysis during the course of optimization. Existing surrogate assisted optimization approaches often use the same approximation model (surrogate) for all objectives and constraints in all regions of the search space. The choice of a type of surrogate model over another is non-trivial and such an a priori choice limits flexibility in representation. In this chapter, we introduce a multi-objective evolutionary algorithm embedded with multiple adaptive spatially distributed surrogates of multiple types. A nondominated sorting genetic algorithm is used as the underlying optimizer. Instead of a single global surrogate, local surrogates of multiple types are constructed around each offspring solution and a multi-objective search is conducted using the best surrogate for the objective and the constraint function. The set of nondominated solutions obtained from each of such local searches are merged to form the potential offspring pool. Top N offspring solutions identified via nondominated sorting and crowding are evaluated using actual analysis resulting in the offspring population. Such an approach offers the flexibility of representation that is often required in practical problems and at the same time capitalizes on the benefits offered by various types of surrogates in different regions of the search space. The performance of the proposed algorithm i.e. surrogate assisted multi-objective optimization algorithm (SAMO) is compared with the baseline Nondominated Sorting Genetic Algorithm II (NSGA-II) to highlight the benefits. The results obtained by SAMO is consistently better (at par with a few) than NSGA-II for the problems presented in this chapter based on an hypervolume metric. One can observe significant benefits in terms of the rate of convergence for SAMO over NSGA-II. Keywords: Multi-objective optimizationevolutionary algorithmssurrogate assisted optimizationlocal surrogates FiguresReferencesRelatedDetailsCited By 3An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary OptimizationQiuzhen Lin, Xunfeng Wu, Lijia Ma, Jianqiang Li and Maoguo Gong et al.1 Aug 2022 | IEEE Transactions on Evolutionary Computation, Vol. 26, No. 4A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective OptimizationSukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb and Erik D. Goodman15 November 2021 | ACM Transactions on Evolutionary Learning and Optimization, Vol. 2, No. 1Constraint-handling techniques in surrogate-assisted evolutionary optimization. An empirical studyMariana-Edith Miranda-Varela and Efrén Mezura-Montes1 Dec 2018 | Applied Soft Computing, Vol. 73 Multi-Objective OptimizationMetrics History KeywordsMulti-objective optimizationevolutionary algorithmssurrogate assisted optimizationlocal surrogatesPDF download