Objectives

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

• Create an active and healthy community to promote theme areas of many-objective optimisation;

• Facilitate the knowledge sharing and collaboration between researchers;

• Exchange experience and promote discussion and contacts between researchers, industrialists and practitioners;

• Organise conferences with IEEE CIS Technical Co-Sponsorship;

• Organise seminars, tutorials, workshops, competition, and special sessions;

• Launch edited volumes, books and special issues in journals.

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:

• Analysis and development of the components of evolutionary algorithms in many-objective optimisation, including search operators, mating selection, environmental selection and population initialisation;

• Comparative studies of various many-objective optimisation techniques;

• Designing and constructing many-objective benchmark test problems;

• Designing quality/performance metrics for many-objective solutions/algorithms;

• Development of meta-heuristic algorithms for many-objective optimisation problems;

• Evolutionary many-objective optimisation methods in search-based software engineering;

• Evolutionary many-objective optimisation methods applied to real-world problems;

• Exact methods from mathematical programming for many-objective optimisation problems;

• Many-objective optimisation in bi-level optimisation problems;

• Many-objective optimisation in combinatorial/discrete optimisation problems;

• Many-objective optimisation in computational expensive optimisation problems;

• Many-objective optimisation in constrained optimisation problems;

• Many-objective optimisation in dynamic environments;

• Many-objective optimisation in large-scale optimisation problems;

• Objective reduction techniques;

• Preference articulation in many-objective optimisation;

• Preference-based search in evolutionary many-objective optimisation;

• Study of parameter sensitivity in many-objective optimisation;

• Theoretical analysis and developments in evolutionary many-objective optimisation;

• Visualisation for decision-making in many-objective optimisation;

• Visualisation for many-objective solution sets;

• Visualisation for search process of meta-heuristic algorithms.

Activities

Current and Planned

• Competition on Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2018), organised by Ran Cheng, Miqing Li, Ye Tian, Xiaoshu Xiang, Xingyi Zhang, Shengxiang Yang, Yaochu Jin and Xin Yao.

• Special Session on Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2018), organised by Ran Cheng, Miqing Li, Rui Wang, and Xin Yao.

• Special Issue on Advanced Methods for Evolutionary Many Objective Optimization, in Information Sciences, organised by Rui Wang and Guohua Wu, 2018.

• Competition on Evolutionary Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2017), organised by Ran Cheng, Miqing Li, Ye Tian, Xingyi Zhang, Shengxiang Yang, Yaochu Jin and Xin Yao.

• Tutorial on Recent Advances in Multi-objective and Many-objective Evolutionary Algorithms, at IEEE Congress on Evolutionary Computation (CEC 2017), presented by Anupam Trivedi and Dipti Srinivasan.

• Special Issue on New Trends in Many-Objective Optimisation, in Int. J. of Bio-Inspired Computation, organised by Gai-Ge Wang, Yi Mei, Mengjie Zhang, and Witold Pedrycz, 2017.

• Special Session on Evolutionary Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2017), organised by Rui Wang, Shengxiang Yang, Sanaz Mostaghim, and Tao Zhang.

• Special Session on Nature-Inspired Constrained Single- Multi- and Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2017), organised by Helio J.C. Barbosa, Yong Wang, and Efren Mezura-Montes.

• Workshop on Visualisation Methods in Genetic and Evolutionary Computation, at Genetic and Evolutionary Computation Conference (GECCO 2017), organised by David Walker, Richard Everson, Jonathan Fieldsend, Bogdan Filipic, and Tea Tusar.

Past

• Plenary Talk on How to Cope with an Increasing Number of Objectives in Optimisation? at IEEE Congress on Evolutionary Computation (CEC 2016), delivered by Xin Yao.

• Special Issue on Evolutionary Many-Objective Optimisation, in IEEE Transactions on Evolutionary Computation, organised by Yaochu Jin, Kaisa Miettinen, and Hisao Ishibuchi, 2016.

• Special Session on Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2016), organised by Hiroyuki Sato and Antonio Lopez Jaimes.

• Special Session on Nature-Inspired Constrained Single- and Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2016), organised by Helio J.C. Barbosa, Yong Wang, and Efren Mezura-Montes.

• Special Session on Efficient Non-dominated Sorting and Pareto Approaches to Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2016), organised by Xingyi Zhang, Ran Cheng, and Yaochu Jin.

• Special Session on Many-Objective Optimisation, at IEEE Congress on Evolutionary Computation (CEC 2015), organised by Hiroyuki Sato.

• Workshop on Visualisation Methods in Genetic and Evolutionary Computation, at Genetic and Evolutionary Computation Conference (GECCO 2016), organised by David Walker, Richard Everson, and Jonathan Fieldsend.

• Workshop on Visualisation Methods in Genetic and Evolutionary Computation, at Genetic and Evolutionary Computation Conference (GECCO 2015), organised by David Walker, Richard Everson, and Jonathan Fieldsend.

Chairs

• Miqing Li (Chair), University of Birmingham, UK.

• Hisao Ishibuchi (Vice Chair), Southern University of Science and Technology, China.

• Carlos A. Coello Coello (Vice Chair), CINVESTAV-IPN, Mexico.

• Ran Cheng (Vice Chair), University of Birmingham, UK.

Members

• Hernan Aguirre, Shinshu University, Japan.

• Slim Bechikh, University of Tunis, Tunisia.

• Xinye Cai, Nanjing University of Aeronautics and Astronautics, China.

• Kalyanmoy Deb, Michigan State University, USA.

• Richard Everson, University of Exeter, UK.

• Jonathan Fieldsend, University of Exeter, UK.

• Raquel Hernandez Gomez, CINVESTAV-IPN, Mexico.

• Yaochu Jin, University of Surrey, UK.

• Mario Koppen, Kyushu Institute of Technology, Japan.

• Ke Li, University of Exeter, UK.

• Xiaohui Liu, Brunel University London, UK.

• Antonio Lopez Jaimes, Autonomous Metropolitan University, Mexico.

• Sanaz Mostaghim, Otto von Guericke University of Magdeburg, Germany.

• Fleming Peter, University of Sheffield, UK.

• Robin C. Purshouse, University of Sheffield, UK.

• Tapabrata Ray, University of New South Wales, Australia.

• Patrick Reed, Cornell University, USA.

• Hemant K. Singh, University of New South Wales, Australia.

• Kay Chen Tan, City University of Hong Kong, Hong Kong.

• Ke Tang, University of Science and Technology of China, China.

• Markus Wagner, University of Adelaide, Australia.

• David Walker, University of Exeter, UK.

• Handing Wang, University of Surrey, UK.

• Rui Wang, National University of Defense Technology, China

• Yi Xiang, Sun Yat-Sen University, China.

• Shengxiang Yang, De Montfort University, UK.

• Gary G. Yen, Oklahoma State University, USA.

• Yuan Yuan, Michigan State University, USA.

• Qingfu Zhang, City University of Hong Kong, Hong Kong.

• Xingyi Zhang, Anhui University, China.

• Liangli Zhen, Sichuan University, China.

• Aimin Zhou, East China Normal University, China.

• Heiner Zille, Otto von Guericke University of Magdeburg, Germany.