IEEE Symposium on Cooperative Metaheuristics (IEEE SCM)

IEEE SSCI 2020 will run as a virtual conference.

The IEEE International Symposium on Cooperative Metaheuristics (SCM 2020) will be held within the 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020) in Canberra, Australia.
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.