Special Session and Competition on Higher Dimension Multimodal Multiobjective Optimization in WCCI 2026
Description：
Multimodal multiobjective optimization (MMO) is a popular topic in evolutionary optimization. With the rapid emergence of large scale structure design, scheduling and machine learning applications, optimization tasks routinely involve tens of hundreds of decision variables and often possesses two or more distinct Pareto optimal sets (PSs) in the decision space. Such multimodal multiobjective optimization problems imply that multiple equivalent PSs corresponding to the same Pareto Front (PF), current multimodal multiobjective evolutionary algorithms (MMOEAs) are primarily evaluated on the benchmarks, in which the dimension of decision variables is limited to 2-10. Therefore, the comprehensive performance of current MMOEAs may not evaluated, such as the convergence performance in the objective space to obtain a high quality Pareto optimal solutions and the diversity performance in the higher dimension decision space. Consequently, it is necessary to investigate higher dimension multimodal multiobjective optimization with these characteristics.
This Special Session and Competition aims to evaluate the performance of multimodal multiobjective evolutionary algorithms for MMOPs, particularly those with higher dimension and multimodal characteristics. Given the prevalence of such problems in practical applications, there is a dearth of research focused on tailored algorithm design.
In this Competition, multiple sets of MMO test problems of different properties are presented. These include problems with tens to hundreds of decision variables whose Pareto fronts are associated with multiple equivalent PSs in the decision space, problems whose PSs differ in shape and spread, the dimensions of decision space and objective space are also scalable. In addition, a fair and appropriate evaluation criterion and reference data are given to assess the performance of different MMOEAs.
This Competition is devoted to the novel approaches, algorithms, and techniques for solving MMOPs. This will help to broaden the field of higher dimension multimodal multiobjective optimization in evolutionary computation and promote more ideas to solve the higher dimension multimodal multiobjective optimization problems.
The Competition will set fixed running times, population size, and maximum number of fitness evaluations for all algorithms. In addition, four performance indicators, Hyper Volume (HV), Inverted Generational Distance (IGD) in objective space (IGDF), IGD in decision space (IGDX), and Pareto Sets Proximity (PSP) are employed to compare the performances of different algorithms. Among the indicators, 1/PSP, and IGDX are used to compare the performance in decision space, while IGDF and HV are used to compare the performance in objective space. The reference data are available on http://www5.zzu.edu.cn/cilab/Code.htm. For all the four indicators, the smaller value means the better performance. This competition provides a baseline of algorithm for contestants to compare, and its code is placed on http://www5.zzu.edu.cn/cilab/Code.htm. All participants are required to submit source codes along with the experimental results. The codes will be checked to avoid cheating. All published papers related with the competition are asked to cite the technical report of the competition.
We encourage all researchers to test their algorithms on the CEC’2026 test suite and to submit their papers to special session on Higher Dimension Multimodal Multiobjective Optimization. In addition, we expect more than twenty participants to take part in the competition. The participants are required to send the final results in the format introduced in the technical report to the organizers and we will present an overall analysis and comparison based on these results. Papers on novel concepts that help us in understanding problem characteristics are also welcome.
Scope：
This special session is devoted to the novel approaches, algorithms and techniques for solving higher dimension multimodal multiobjective optimization problems. The main topics of the special session are:
•Evolutionary algorithms for higher dimension multimodal multiobjective optimization
•Surrogate techniques for higher dimension multimodal multiobjective optimization
•Machine learning methods helping to solve higher dimension multimodal multiobjective optimization problems
•Memetic computing for higher dimension multimodal multiobjective optimization
•Niching techniques for higher dimension multimodal multiobjective optimization
•Parallel computing for higher dimension multimodal multiobjective optimization
•Design methods for higher dimension multimodal multiobjective optimization test problems
•Decision making in higher dimension multimodal multiobjective optimization
• Related theory analysis
• Applications
Submission Guidelines (Results&Papers)：
Results
Please test your algorithm on the test problems in “WCCI2026 HDMMO”. The results, codes, and the description for your algorithm and results need to be submitted to yuecaitong@zzu.edu.cn. We will present an overall analysis and comparison based on these results.
Papers
Please follow the submission guideline from the IEEE WCCI 2026 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the novel approaches, algorithms and techniques for solving higher dimension MMOPs. All papers accepted and presented at IEEE WCCI 2026 will be included in the conference proceedings published by IEEE Xplore, which are typically indexed by EI.
Important Dates：
Paper submission: January 31, 2026
Notification of acceptance: March 15, 2026
Final paper submission: April 15, 2026
Organizers：
Jing Liang: liangjing@zzu.edu.cn
Caitong Yue: yuecaitong@zzu.edu.cn
Kunjie Yu: yukunjie@zzu.edu.cn
Ying Bi: yingbi@zzu.edu.cn
Hui Song: hui.song@rmit.edu.au