A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Chugh, T., Sindhya, K., Hakanen, J., & Miettinen, K. (2019). A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Computing, 23(9), 3137-3166. https://doi.org/10.1007/s00500-017-2965-0
Published inSoft Computing
© 2017 Springer-Verlag GmbH Germany, part of Springer Nature
Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available. ...
ISSN Search the Publication Forum1432-7643
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization Chugh, Tinkle; Jin, Yaochu; Miettinen, Kaisa; Hakanen, Jussi; Sindhya, Karthik (Institute of Electrical and Electronics Engineers, 2018)We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed ...
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
Handling expensive multiobjective optimization problems with evolutionary algorithms Chugh, Tinkle (University of Jyväskylä, 2017)Multiobjective optimization problems (MOPs) with a large number of conﬂicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations ...
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies Chugh, Tinkle; Allmendinger, Richard; Ojalehto, Vesa; Miettinen, Kaisa (Association for Computing Machinery (ACM), 2018)We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization ...
A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods Tabatabaei, Mohammad; Hakanen, Jussi; Hartikainen, Markus; Miettinen, Kaisa; Sindhya, Karthik (Springer Berlin Heidelberg; International Society for Structural and Multidisciplinary Optimization, 2015)Computationally expensive multiobjective optimization problems arise, e.g. in many engineering applications, where several conflicting objectives are to be optimized simultaneously while satisfying constraints. In many ...