Objective Space-based Population Generation to Accelerate Evolutionary Algorithms for Large-scale Many-objective Optimization

Qi Deng, Qi Kang, Liang Zhang, Meng Chu Zhou, Jing An

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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. Differently from them, this work proposes an objective space-based population method to generate 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.

Original languageEnglish (US)
JournalIEEE Transactions on Evolutionary Computation
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics

Keywords

  • decision variables decomposition
  • large-scale evolution
  • many-objective evolution.
  • Objective space mapping

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