A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Abstract

Surrogate-assisted evolutionary algorithms have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing surrogate-assisted evolutionary algorithms are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogateassisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of manyobjective optimization test problems.

Data-Driven Evolutionary Optimization: An Overview and Case Studies

“…Using surrogate models: Once surrogate models are selected, the next question is how to use them in the EA. For instance, approximating objective functions [10], classifying samples according to their fitness [78], predicting ranks [79], or hypervolume [80] or approximating a scalarizing function by converting a multi-objective optimization problem to a single-objective problem [71], [81] and approximating the PF [82] are possible ways of using a surrogate model.…”

“…These are dimensions in the objective and decision spaces, handling constraints, and mixed-integer or combinatorial optimization problems. Some on-line optimization algorithms, e.g., K-RVEA [12], [91], CSEA [78], and SL-PSO [20] have been proposed to tackle these challenges. However, many real-world on-line data-driven problems are constrained [92]- [97] and / or of mixed-integer decision variables [98]- [104].…”

Model-based evolutionary algorithms: a short survey

“…Later in 2017, Zhang et al trained a classifier based on a regression tree or a knearest-neighbour (KNN) to distinguish good solutions from bad ones [86]. Recently, Pan et al proposed a classificationbased surrogate-assisted evolutionary algorithm (CSEA) to learn the dominance relationship between a candidate solution and a set of reference solutions [87].…”

A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems

“…In individualbased model management strategies, promising and uncertain solutions according to the surrogate model [26]- [28] are evaluated using the expensive objective functions. A large number of SAEAs for single-objective optimization [6], [25], [29], [30], multi-objective optimization [12], [22], [31], [32], and many-objective optimization [13], [33] have been proposed.…”