A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

• Published in: Soft computing (Berlin, Germany) Vol. 23; no. 9; pp. 3137 - 3166
• Main Authors: Chugh, Tinkle, Sindhya, Karthik, Hakanen, Jussi, Miettinen, Kaisa
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2019; Springer Nature B.V
• Subjects: Approximation; Artificial Intelligence; Computation; Computational Intelligence; Control; Engineering; Evolutionary algorithms; Genetic algorithms; Hierarchies; Mathematical analysis; Mathematical Logic and Foundations; Mechatronics; Metamodels; Methodologies and Application; Multiple objective analysis; Objectives; Optimization; Robotics; Machine learning; Pareto optimality; Multicriteria optimization; Computational cost; Response surface approximation; Surrogate; Metamodel
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-017-2965-0

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.