The IEEE International Symposium on Cooperative Metaheuristics (SCM 2023) will be held within the 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023) in Mexico City, Mexico.
Cooperative Metaheuristics refer to the area of having multiple metaheuristic algorithms cooperating with each other to solve an optimization problem. Cooperation could be classified as implicit or explicit. In implicit decomposition, multiple instances implicitly tackle different areas of the search space using various initialization, control parameter settings, etc. In explicit decomposition, each instance operates in a dedicated subspace either by dividing the entire search space among instances or dividing the problem variables (i.e., cooperative coevolution). Many cooperative search algorithms have produced remarkably effective solutions to continuous, discrete, combinatorial, and multi-objective problems in many fields.
Topics
This symposium aims at presenting the latest developments of cooperative metaheuristics techniques, exchanging new ideas and discussing open research questions and future directions. Original contributions that provide novel theories, frameworks, and applications to this topic are very welcome. Potential topics include, but are not limited to:
- Theoretical analysis (modeling, stability, convergence, etc.) of cooperative or competitive algorithms.
- Comparative analysis and performance studies of different various cooperative models.
- Control, tuning, and adaptation strategies of cooperative or competitive algorithms.
- Parallelized/Hardware implementations (clusters, GPUs, etc.) of cooperative algorithms.
- Novel cooperative metaheuristic techniques (framework, ensembles, surrogate-assisted, problem decomposition, information exchange, etc.).
- Hybrid cooperative or competitive algorithms.
- Different types of optimization problems: constrained and unconstrained, single, multi – and many-objective, continuous and discrete optimization, mixed type decision variables, dynamic optimization, and large-scale optimization.
- Real-world applications
Symposium Chairs
- Mohammed El-Abd
melabd@auk.edu.kw
American University of Kuwait - Shi Cheng
cheng@snnu.edu.cn
Shaanxi Normal University, China - Seyedali Mirjalili
seyedali.mirjalili@uni-obuda.hu
University Research and Innovation Center, Obuda University, Hungary - Diego Oliva
diego.oliva@cucei.udg.mx
Universidad de Guadalajara, CUCEI, Mexico
Programme Committee
- Iyad Abo Doush, American University of Kuwait
- Shangce Gao, University of Toyama, Japan
- José Fernando Camacho Vallejo, Universidad Autónoma de Nuevo León (UANL), Mexico
- Azam Asilian Bidgoli, Ontario Tech University, Canada
- Yu Xue, Nanjing University of Information Science and Technology, China
- Mohamed G. Omran, Gulf University of Science and Technology, Kuwait