Special Session on Massively Parallel Evolutionary Computation in the Smart City Era (MPEC)

At 2020 IEEE Congress on Evolutionary Computation, Glasgow, Scotland, UK


The smart city concept integrates information and communication technology, and various physical devices connected to the cloud network to optimize the efficiency of city operations and services and connect to citizens. Therefore, most of real-world problems in the smart city are multimodal interface problems and/or multi-objective optimization problems involving several conflicting objectives.

On the other hand, cloud systems may even offer tens of thousands of virtual machines, terabytes of memories and exa-bytes of storage capacity. Current trend toward many-core architecture increases the number of cores even more dramatically: we may have more than a million of cores to offer extremely massive parallelization. Next, the evolution of modern computational science, the field of evolutionary computing is shifting rapidly to the big era where optimization problems can be characterized following different complex and cross-dependent aspects: a large number of decision variables, a large number of conflicting objectives, expensive evaluation functions, simulation-dependent problem formulations, uncertain and scenario-based models, multi- disciplinary models, non-smooth and multi-modal black-box setting, etc. These characteristics give rise to difficult challenges being beyond the ability of commonly used optimization algorithms.

The purpose of this special session is to promote the design, study, and validation of generic approaches addressing the big nature of nowadays optimization problems through the investigation of appropriate evolutionary techniques that can fit in the large scale and distributed nature of modern compute facilities.

The special session will be a nice opportunity for researchers in the evolutionary and parallel optimization filed to exchange their recent ideas and advances. In this respect, we are welcoming high quality papers in all theoretical, developmental, implementation, and applied aspects. The focus is on original research contributions eliciting the main design principles that lead to efficient decentralized evolutionary search procedures that can scale with respect the available compute resources while effectively addressing the big nature of target optimization problems.


The topics of interests include (but are not limited to) the following issues:
  • Decentralized evolutionary optimization techniques and paradigms with clear parallel potential, e.g., divide-and-conquer techniques, aggregation and grouping-based algorithms, novel decomposition-based techniques in decision and objective space, novel parallel models
  • Theoretical and/or empirical studies on the scalability of parallelizing evolutionary optimization algorithms, e.g., shared memory, message-passing and hybrid algorithms
  • Computational studies on parallelizing evolutionary algorithms in complex settings, e.g., expensive and simulation-based optimization, surrogate-based algorithms, noisy functions, scenario-based and robust optimization
  • Design, implementation and case-study of massively parallel and large scale distributed evolutionary algorithms, e.g., on GPUs, Phi Processors, Clusters, Grids. Computational investigations on the solving of real-world big optimization problems
  • Distributed multi/many objective evolutionary algorithms involving several conflicting objectives
  • Parallel and distributed evolutionary algorithms in the smart city

Submission deadline:

Please follow the submission deadline from the IEEE WCCI 2020 submission website.

Important Dates:

Organizers (Contact information):


CEC 2020 SS-16    Program Committee: