Special Session and Competition on Constrained Multiobjective Optimization in WCCI 2024
Description：
Constrained multiobjective optimization (CMO) is a popular topic in evolutionary optimization. There are several benchmark test suits and real-world test instances of CMO problems are proposed. The main thrust of solving these problems is to obtain a well-distributed constrained Pareto front (CPF). However, in several practical CMO problems, there exist two or more global or local constrained Pareto optimal sets (CPSs) corresponding to one of the same CPF, which is considered to have multimodal characteristics. An effective constrained multiobjective optimization evolutionary algorithm (CMOEA) should be capable of addressing diverse CMO problems. Currently, there is no test set available to assess the proficiency of CMOEAs in simultaneously handling single-modal and multi-modal CMO problems. Consequently, it is necessary to investigate constrained multiobjective optimization with these characteristics.
The Special Session and Competition aim to promote the development of constrained multiobjective evolutionary algorithms (CMOEAs) for CMO problems, particularly those with single-modal and multimodal characteristics. Given the prevalence of such problems in practical applications, there is a dearth of research focused on tailored algorithm design.
The Special Session is devoted to novel approaches, algorithms, and techniques for solving CMO problems. This will help to broaden the field of constrained multiobjective optimization in evolutionary computation and promote more ideas to solve the constrained multiobjective optimization problems.
We encourage all researchers to test their algorithms on the CEC’24 test suite and to submit their papers to the special session on Constrained 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 understand problem characteristics are also welcome.
Scope：
This special session is devoted to the novel approaches, algorithms and techniques for solving constrained multiobjective optimization problems. The main topics of the special session are:
• Evolutionary algorithms for constrained multiobjective optimization
• Evolutionary algorithms for constrained multimodal multiobjective optimization
• Surrogate techniques for constrained multiobjective optimization
• Machine learning methods helping to solve constrained multiobjective optimization problems
• Memetic computing for constrained multiobjective optimization
• Niching techniques for constrained multiobjective optimization
• Parallel computing for constrained multiobjective optimization
• Design methods for constrained multiobjective optimization test problems
• Decision making in constrained multiobjective optimization
• Related theory analysis
• Applications
Submission Guidelines (Results&Papers)：
Results
Please test your algorithm on the test problems in “CEC2024 CMO”. 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 2024 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 CMOPs. All papers accepted and presented at IEEE WCCI 2024 will be included in the conference proceedings published by IEEE Xplore, which are typically indexed by EI.
Important Dates：
Paper submission: 15 January 2024
Notification of acceptance: Mar 15, 2024
Final paper submission: May 1, 2024
Organizers：
Jing Liang: liangjing@zzu.edu.cn
Caitong Yue: yuecaitong@zzu.edu.cn
Boyang Qu: quboyang@zut.edu.cn
Kunjie Yu: yukunjie@zzu.edu.cn
Ying Bi: yingbi@zzu.edu.cn