Introduction

Over the past decade, there has been rising interest in research on dynamic multiobjective optimisation. This is a challenging yet highly important topic that addresses problems with multiobjective and time-varying characteristics. Due to the existence of dynamics, dynamic multiobjective optimization problems (DMOPs) are inherently more complex and demanding than static multiobjective problems, presenting considerable challenges to evolutionary algorithms (EAs) when solving them. Broadly speaking, DMOPs bring about at least three main challenges. First, environmental changes are hard to detect. If they go undetected, they can mislead the search process because nondominated solutions identified for the previous environment may no longer be valid in the current one. Second, Diversity, which acts as the primary driving force of population-based algorithms, is highly sensitive to dynamics. The dynamic nature of DMOPs, which is defined by irregular changes multimodality and discrete Pareto optimal sets (PSs) or fronts (PFs), makes the optimisation process significantly more complex. Finally, algorithms often face tight time constraints when responding to environmental changes. These time limits on DMOPs require algorithms to balance diversity and convergence effectively. This balance allows them to deal with environmental changes promptly and closely track time-varying PSs or PFs. These challenges underline the need to develop more complex and comprehensive test problems. Doing so will encourage the creation of innovative methodologies to overcome these difficulties.

Benchmark problems are highly significant to algorithm analysis, enabling algorithm designers and practitioners to gain a clearer understanding of the strengths and weaknesses of evolutionary algorithms. In dynamic multi-objective optimisation, several widely used test suites exist, including FDA, dMOP and JY. However, these problem suites oversimplify the complexity of variations in real-world problems, capturing only specific facets of actual scenarios. For instance, the FDA and dMOP functions pose no detection challenges for algorithms. Environmental changes in these problems can be readily identified through a single re-evaluation of a random population member, which is far simpler than real-life environmental shifts. It is also acknowledged that most existing dynamic multi-objective optimisation problems (DMOPs) are direct adaptations of popular static test suites, such as ZDT and DTLZ. Consequently, these DMOPs exhibit considerable similarity in their problem characteristics, making them of limited value for comprehensive algorithm analysis. Furthermore, a concerning feature of most existing DMOPs is that static problem properties dominate over the dynamic elements to an excessive degree. A problem property, such as strong variable dependency, that presents challenges for static multi-objective optimisation may not be well-suited to dynamic multi-objective optimisation. One key reason is that algorithmic underperformance on DMOPs often stems from the static property rather than the presence of dynamics. Using such DMOPs can therefore lead to misleading inferences about algorithm performance. Additionally, most benchmark designs are founded on the premise that environments remain similar before and after a change. In real-world contexts, however, many DMOPs involve erratic environmental shifts. In such cases, the search directions employed by EAs for the current environment may be unsuitable for the new one, particularly when the PS of the new environment diverges substantially from, and in the worst-case scenario, even points in the opposite direction to, that of the current environment. In summary, a suite of diverse and unbiased benchmark test problems is urgently needed in the field to support the systematic investigation of evolutionary algorithms.



Competition Guideline

In this competition, a total of 20 benchmark functions are introduced [1-5], covering representative types of DMOPs (continuous, and constrained) with diverse properties found in various real-world scenarios, such as irregular changes of PS or PF, time-dependent PF/PS geometries, disconnectivity, degeneration, detectability, and a changing number of decision variables and/or objective functions. Through suggesting a set of benchmark functions with a good representation of various real-world scenarios, we aim to promote the research on evolutionary dynamic multiobjective optimisation. All the benchmark functions have been implemented in MATLAB code.

The benchmarks used for competition are detailed in the following technical report download here, which includes the necessary information to understand the problem, how the solutions are represented, and how the fitness function is evaluated. Please contact Dr Xiaozhong Yu. (xzyu@smail.xtu.edu.cn) if you encounter any problem.

There are three tracks of competition, briefly described as follows:

Track 1: Dynamic Unconstrained Multi-Objective Optimisation

Track 2: Dynamic Constrained Multi-Objective Optimisation

Please feel free to choose either one or multiple tracks for competition. However, you should make a separate submission for each track you have chosen.

J. Zou, X. Yu, H. Bai, S. Yang, S. Jiang, X. Zhao, J. Chen, Z. Hou, Y. Hu and, " Benchmark Problems for WCCI’2026 Competition on Dynamic Multiobjective Optimisation," technical Report., Netherlands, November, WCCI.

All the benchmark functions have been implemented in MATLAB code (download here).Source code for sampling on the PF is also provided (download here).Please note that, the sampled points of PF should be truncated properly to guarantee uniformity (the k-nearest neighbor method in SPEA2 could be used for this purpose). Your results of the competition can be submitted in the form of a brief technical report, which should be sent directly to Dr Xiaozhong Yu. Submissions in both forms will be considered as entries, therefore be ranked according to the competition evaluation criteria.

References
[1] Zou J, Hou Z, Jiang S, et al. Knowledge Transfer With Mixture Model in Dynamic Multi-Objective Optimization[J]. IEEE Transactions on Evolutionary Computation, 2025. [2]Jiang S, Yang S, Yao X, et al. Benchmark functions for the cec'2018 competition on dynamic multiobjective optimization[R]. Newcastle University, 2018.

[3] Jiang S, Kaiser M, Yang S, et al. A scalable test suite for continuous dynamic multiobjective optimization[J]. IEEE transactions on cybernetics, 2019, 50(6): 2814-2826.

[4] Jiang S, Yang S. Evolutionary dynamic multiobjective optimization: Benchmarks and algorithm comparisons[J]. IEEE transactions on cybernetics, 2016, 47(1): 198-211.

[5] Guo Y, Chen G, Yue C, et al. Benchmark problems for CEC2023 competition on dynamic constrained multiobjective optimization[J]. Proc. CEC2023 Competition, 2023: 1-12.



Important dates Result submission deadline: June 15, 2026

Note: Please send your results directly to Dr Xiaozhong Yu (xzyu@ smail.xtu.edu.cn)

Competition Organizers:

Name: Juan Zou

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: zoujuan@xtu.edu.cn

Short Bio:Juan Zou is curretly a Professor in Computational Intelligence and Director of Hunan Engineering Research Center of Intelligent System Optimization and Security, Xiangtan University, Xiangtan, China. She has over 100 publications. Her current research interests include evolutionary and genetic algorithms, computational intelligence in dynamic and uncertain environments, neural network architecture search, and relevant real-world applications. She is an Associate Editor or Editorial Board Member of international journals, including IEEE Transctions Evolutionary Computation and Engineering Applications of Artificial Intelligence.

Name: Xiaozhong Yu

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: xzyu@smail.xtu.edu.cn

Short Bio:Xiaozhong Yu is currently pursuing the Ph.D. degree at the School of Computer Science, Xiangtan University, Xiangtan, China. Her current research interests include multi-objective optimisation, data-driven dynamic multi-objective optimization, and their related applications.

Name: Hui Bai

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: huibai@xtu.edu.cn

Short Bio:Hui Bai is currently a lecturer of School of Computer Science, Xiangtan University, Xiangtan, China. In recent years, she has published 6 first-authored papers in top journals and conferences in the field of artificial intelligence. She once served as the Web Chair of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), an international conference in the field of evolutionary computation; as the Social Media Chair of the Shenzhen Chapter of the IEEE Computational Intelligence Society. Her main research directions include: evolutionary computation, evolutionary reinforcement learning, data-driven intelligent optimization, and robot control.

Name: Shengxiang Yang

Affiliation:Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, Leicester, U.K., Email: syang@dmu.ac.uk

Short Bio:Shengxiang Yang got his PhD degree in 1999 from Northeastern University, China. He is now a Professor of Computational Intelligence (CI), Director of the Centre for Computational Intelligence, and Deputy Director of the Institute of Artificial Intelligence, De Montfort University, UK. He has worked extensively for 20 years in the areas of CI methods, including EC and artificial neural networks, and their applications for real-world problems. He has over 470 publications with a H-index of 76 and over 22,000 citations (Google Scholar). His work has been supported by UK research councils, EU FP7 and Horizon 2020, Chinese Ministry of Education, and industry partners, with a total funding of over £2M, of which two UK EPSRC standard research projects have been focused on EC for DOPs. He is now a Vice President of Asia Computational Intelligence Society (ACIS) and has served as an Associate Editor or Editorial Board Member of over ten international journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, Enterprise Information Systems, and Soft Computing, etc. He was the founding chair of the Task Force on Intelligent Network Systems (TF-INS, 2012-2017) and the chair of the Task Force on EC in Dynamic and Uncertain Environments (ECiDUEs, 2011-2017) of the IEEE CI Society (CIS). He has organised/chaired over 70 workshops and special sessions relevant to ECiDUEs for several major international conferences. He was the founding co-chair of the IEEE Symposium on CI in Dynamic and Uncertain Environments. He has co-authored/co-edited three books, and co-edited over 10 proceedings and journal special issues. He has given over 30 invited keynote speeches or tutorials at international conferences and workshops.

Name: Shouyong Jiang

Affiliation:Department of Artificial Intelligence, School of Automation, Central South University. Email: sjiang@csu.edu.cn

Short Bio:Shouyong Jiang is currently a Professor in at Department of Artificial Intelligence, School of Automation, Central South University. He has over 50 publications. His current research interests include evolutionary multiobjective optimisation in static and dynamic environments, machine learning, network modelling and analysis, and AI4Science. He served as an Associate Editor or Editorial Board Member of several journals, including CAAI Transactions on Information Technology, iMeta and Algorithms. He also organized serval special sessions and competitions on conferences, including CEC2023 Optimization Methods in BioInformatics and BioEngineering and CEC2018 Competition on Dynamic Multiobjective Optimization. He is the recipient of 2021 IEEE CIS Outstanding Dissertation Award in recognition of his contribution to the field of dynamic multiobjective optimization.

Name: Xinjie Zhao

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: 1936862341@qq.com

Short Bio:Xinjie Zhao is currently pursuing the bachelor degree at the School of Computer Science, Xiangtan University, Xiangtan, China. His current research interests include dynamic multi-objective optimisation.

Name: Junhao Chen

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: 1146696400@qq.com

Short Bio:Junhao Chen is currently pursuing the bachelor degree at the School of Computer Science, Xiangtan University, Xiangtan, China. His current research interests include data-driven dynamic multi-objective optimisation.

Name: Zhanglu Hou

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: zhanglhou@163.com

Short Bio:Zhanglu Hou is currently pursuing the Ph.D. degree at the School of Computer Science, Xiangtan University, Xiangtan, China. His current research interests include multi-objective optimisation,decision-making in uncertain and dynmaic environment systems, and their applications.

Name: Yaru Hu

Affiliation:School of Computer Science, Xiangtan University, Xiangtan, China. Email: yaruhu@xtu.edu.cn

Short Bio:Yaru Hu is currently an Associate Professor of School of Computer Science, Xiangtan University, Xiangtan, China. She has over 25 publications and has received the 2024 Hunan Provincial Outstanding Dissertation Award in recognition of her contribution. Her current research include evolutionary multiobjective optimisation in static and dynamic environments, machine learning.