Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization

Document Type

Article

Publication Date

4-1-2023

Abstract

The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objective space-based population generation method to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions. It introduces three new objective vector generation methods and uses a linear mapping method to tightly connect objective space and decision one to jointly determine new-generation solutions. A loop can be formed directly between two spaces, which can generate new solutions faster and use more feedback information in the objective space. In order to demonstrate the performance of the proposed algorithm, this work performs a series of empirical experiments involving both large-scale decision variables and many objectives. Compared with the state-of-the-art traditional and large-scale algorithms, the proposed method exceeds or at least reaches its peers' best level in overall performance while achieving great saving in execution time.

Identifier

85128613576 (Scopus)

Publication Title

IEEE Transactions on Evolutionary Computation

External Full Text Location

https://doi.org/10.1109/TEVC.2022.3166815

e-ISSN

19410026

ISSN

1089778X

First Page

326

Last Page

340

Issue

2

Volume

27

Grant

2021-cyxt2-kj10

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS