IEEE CIS Task Force on Many-Objective Optimisation


Many-objective optimisation refers to a class of optimisation problems that have more than three objectives. The last decade has witnessed the emergence of many-objective optimisation as a booming topic in a wide range of complex modern real-world scenarios. However, in contrast to conventional multi-objective optimisation which involves two or three objectives, many-objective optimisation poses far great challenges to the area of nature-inspired search algorithms. On the one hand, the ineffectiveness of Pareto dominance, aggravation of the conflict between convergence and diversity, and inefficiency of recombination operation, along with rapid increase of time or space requirement and parameter sensitivity, have been significant barriers to the design of many-objective search algorithms. On the other hand, the infeasibility of solutions' direct observation, difficulty of the representation of the trade-off surface, and difficulty of understanding the relationship between objectives and articulating preferences leads to serious challenges in algorithm performance investigation, comparison and decision-making process. All of these suggest a pressing need for new methodologies in many-objective optimisation.

The objective of this task force is to promote the research on many-objective optimisation. It includes

Anticipated Interests

This task force will focus on all aspects in many-objective optimisation, including theory, practice and applications covering all paradigms in the high-dimensional space. Topics of interest include but are not limited to the following:


Current and Planned Past