Multiobjective Multifactorial Optimization in Evolutionary Multitasking

Abhishek Gupta, Yew Soon Ong, Liang Feng, Kay Chen Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

277 Citations (Scopus)


In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

Original languageEnglish
Article number7464295
Pages (from-to)1652-1665
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number7
Publication statusPublished - Jul 2017
Externally publishedYes


  • Evolutionary multitasking
  • memetic computation
  • multiobjective optimization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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