Kriging-assisted indicator-based evolutionary algorithm for expensive multi-objective optimization

doi:10.1016/j.asoc.2023.110736

Kriging-assisted indicator-based evolutionary algorithm for expensive multi-objective optimization

2023-11-01

6

PRE

AI

DOI：10.1016/j.asoc.2023.110736

Original

Original request for help

Save

Abstract

En 中文

Selection mechanisms based on performance indicators are popular for solving multi-objective optimization problems (MOPs), which can provide a comprehensive evaluation of the convergence and diversity of candidate solutions. However, these mechanisms recently encounter difficulties when tackling computationally expensive MOPs attributed to the restricted quantity of real function evaluations. To tackle this problem, this paper proposes a Kriging-assisted indicator-based evolutionary algorithm. The primary concept is to utilize the Kriging model to approximate computationally expensive objective functions, thereby reducing the computational cost, and to employ the R2 indicator to assess the quality of candidate solutions. In addition, a dual selection mechanism based on the lower confidence bound is proposed as the model management strategy to balance exploration and exploitation. The experimental results demonstrate that the proposed algorithm is competitive when compared to five representative algorithms.& COPY; 2023 Elsevier B.V. All rights reserved.

Keywords:

Expensive multi-objective optimization

Kriging model

R2 indicator

Lower confidence bound

Model management

Kriging-assisted indicator-based evolutionary algorithm for expensive multi-objective optimization

Abstract

Journal

Organization