大家好！我们计划在IEEE进化计算国际会议IEEE World Congress on Computational Intelligence/ Congress on Evolutionary Computation上举办“单/多目标-无约束/约束数值优化”的Special Session，将接收关于解决单/多目标数值优化或者实际应用问题的论文，请大家多多赐稿！
投稿截止日期为：2026年1月31日 （最终期限）
论文录用日期为：2026年3月15日
参会日期为：2026年6月21日-26日
投稿时请选择CEC-SS：Special session associated with Competition on Numerical Optimization
欢迎各位专家学者学生们踊跃投稿！谢谢！
不投稿也可以直接参加竞赛
CEC是IEEE计算智能学会每年主办的国际会议，汇聚了来自世界各国的进化计算专家学者，并且有很多精彩的keynotes，tutorials和panel discussions。该会议论文集将被IEEE Xplore收录，并包括在所有主流的索引里（例如EI index，DBLP）。
今年CEC将在美丽的荷兰举办，是非常好的参会机会！
以下是Call for paper详情，如有任何问题，请联系Ponnuthurai Nagaratnam Suganthan（p.n.suganthan@qu.edu.qa)
详细信息如下：
Call for Papers:
Special Session on Competitions on Numerical Optimization
Jun 21-26, 2026, Netherlands, China
https://attend.ieee.org/wcci-2026/
Overview:
Constrained optimization is a key area of optimization theory and practice, where the objective is to find the optimal solution for a problem subject to certain constraints. These constraints can be equality or inequality conditions that restrict the values the decision variables can take. In real-world applications, many optimization problems require finding a balance between optimizing a given objective function and satisfying a set of constraints. Constrained optimization is widely used in fields such as engineering design, resource allocation, and operations research. The complexity of constrained optimization arises from the interaction between the objective function and the constraints, which may not always be straightforward or easy to solve.
Bound constrained single-objective optimization focuses on optimizing a single objective function while ensuring all variables remain within their specified bounds. The presence of bounds can significantly impact the optimization process. When variables hit their bounds during optimization, special handling is required to determine whether to move along the boundary or search for better solutions in other directions. The interaction between the objective function and the bound constraints can create challenging scenarios, particularly when the optimal solution lies on or near the boundaries of the feasible region. Additionally, the presence of bounds can introduce non-smoothness at the boundaries, which may complicate the use of gradient-based optimization methods.
Constrained single-objective optimization involves optimizing a single objective function while adhering to a set of constraints. Sometimes, the objective function and the constraints may conflict, making it impossible to find a feasible solution, or the feasible region may be very small, leading to a difficult search for the optimal solution. In many practical problems, constraints may be nonlinear, non-differentiable, or irregular, making the optimization problem more difficult to solve using standard methods. In addition, the optimization process may get trapped in local optima, especially when dealing with complex, nonlinear objective functions or constraints, preventing the identification of the global optimum.
For bound constrained multi-objective optimization, multiple conflicting objectives must be optimized simultaneously while respecting the bound constraints on all variables. The bound constraints add another layer of complexity to the already challenging task of finding Pareto-optimal solutions. The feasible region defined by the bounds may limit the achievable trade-offs between different objectives, and the presence of bounds can affect the distribution and quality of the Pareto front. This type of problem often arises in practical applications where multiple performance metrics need to be optimized while keeping system parameters within their operational limits or physical constraints.
Constrained multi-objective optimization involves optimizing multiple conflicting objective functions simultaneously, while also satisfying a set of constraints. Unlike single-objective optimization, multi-objective optimization does not have a unique optimal solution but rather a set of Pareto-optimal solutions, where no objective can be improved without sacrificing another. In this case, the goal is to find the optimal trade-off solutions between multiple objectives.
Aims and scope:
In this competition, four tracks have been established: Bound Constrained Single-Objective Numerical Optimization, Constrained Single-Objective Numerical Optimization, Bound Constrained Multi-Objective Numerical Optimization, and Constrained Multi-Objective Numerical Optimization. Each track comprises multiple benchmark problems designed to test algorithms' performance. The evaluation will focus on both the algorithms' efficiency in handling (bound) constraints and their effectiveness in finding optimal solutions within the search space. Each algorithm will be evaluated across multiple independent runs to ensure statistical significance. Participants are encouraged to develop innovative strategies that advance the state-of-the-art in handling (bound) constraints for single or multi-objective optimization problems.
This special session will serve as a platform for researchers to exchange ideas and present novel approaches. Authors are invited to submit their original works to this special session. Topics of interest include, but are not limited to, novel evolutionary algorithms for (bound) constrained optimization, other machine learning methods or mathematical methods for bound constrained optimization, and real-world applications involving bound constraints in both single and multi-objective contexts.
Important dates:
Special Session paper submissions: January 31, 2026
Special Session paper acceptance notifications: March 15, 2026
Conference dates: June 21-26, 2025
All submissions will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality, and clarity.
Note！
If you do not intend to submit a paper, you can also enter the competition directly by submitting your results in the format required in the technical reports.
Technical Reports: https://github.com/P-N-Suganthan/2026-CEC
Competition results submission date: Early May, 2026
The results of the Bound Constrained Single-Objective Optimization Problems please send to Hongyu Lin: linhongyuayu@163.com
The results of the Constrained Single-Objective Optimization Problems please send to Xuanxuan Ban: xxuanban@163.com
The results of the Bound Constrained Multi-Objective Optimization Problems please send to Peng Chen: ty1220899231@163.com
The results of the Constrained Multi-Objective Optimization Problems please send to Kangjia Qiao: qiaokangjia@yeah.net
Organizers：
Prof. Ponnuthurai Nagaratnam Suganthan
Email: p.n.suganthan@qu.edu.qa
Prof. Kenneth V. Price
Prof. Jing Liang
Email: liangjing@zzu.edu.cn
Prof. Caitong Yue
Email: yuecaitong@zzu.edu.cn