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IEEE CIS Task Force on Decomposition-based Techniques in Evolutionary Computation


As the name suggests, the basic idea of the decomposition-based technique is to transform the original complex problem into simplified subproblem(s) so as to facilitate the optimization. Decomposition-based techniques have been widely used for solving both single- and multi-objective optimization problems. More specifically, in single-objective optimization, especially for the large-scale scenarios, which consider a tremendous amount of decision variables, the decomposition-based technique contains three aspects: 1) analyzing and understanding the fitness landscape and modularity structure of the underlying problem; 2) decomposing the original complex problem into several loosely coupled or independent subproblems based on the learnt characteristics; 3) using a meta-heuristic to solve these subproblems in a sequential or concurrent manner. As for multi-objective optimization, the decomposition means to decompose the original multi-objective optimization problem into a number of single-objective optimization sub-problems (or simple multi-objective optimization problems) and then uses a meta-heuristic to optimize these sub-problems simultaneously and collaboratively. In this big data era, the decomposition-based techniques used for both single- and multi-objective optimization can be sythesized to address the challenges posed by the curse of dimensionality, i.e., many objectives and large scale variables.

The key objective of this task force it to generalize the decomposition-based idea and to promote its related research, including its development, education and understanding of its sub topic areas.

The main objectives of the task force can be summarized as follows:

  • create an active and healthy community to promote theme areas of decomposition-based techniques

  • make student, researchers, end-users, developers, and consultants aware of the state-of-the-art

  • promote the use of decomposition-based methodologies/techniques and tools

  • organize conferences/workshop with IEEE CIS Technical Co-Sponsorship

  • organize tutorials, workshops and special sessions

  • launch edited volumes, books, and special issues in journals

Anticipated Interests

This task force will focus on all aspects, including theory, practice and applications, of the decomposition-based technique in evolutionary computation for solving both single-, multi- and many-objective optimization problems.

Topics of interest including but are not limited to the following:

  • Design of novel weight vector generation methods

  • Development of new decomposition methods

  • Design of novel computational resource allocation strategies

  • Integration of new reproduction operators

  • Investigation of novel mating selection and replacement procedures

  • Understanding of the relationship between subproblems and solutions

  • Development of novel decomposition-based MOEAs

  • Hybridization of dominance- and decomposition-based approaches

  • Incorporation of user-preferences in decomposition-based MOEAs

  • Extension to many-objective optimization problems

  • Extension to constrained multi- and many-objective optimization problems

  • Design of novel methods to analyze and understand the modularity structure

  • Design of novel cooperative coevolution for large-scale optimization problems

  • Theoretical analysis of the decomposition-based methods



Past Activities



  • Ke Li (Chair), College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.

  • Qingfu Zhang (Vice chair), Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China.

  • Anupam Trivedi (Vice chair), Department of Electrical & Computer Engineering, National University of Singapore, Singapore.

Members (Sorted alphabetically based on the surname)