IEEE CEC Special Session on Large Scale Global Optimization

CEC 2021

Evolutionary Computation

In the past two decades, many evolutionary algorithms have been developed and successfully applied for solving a wide range of optimization problems. Although these techniques have shown excellent search capabilities when applied to small or medium sized problems, they still encounter serious challenges when applied to large scale problems, i.e., problems with several hundreds to thousands of variables. This is due to the Curse of dimensionality, as the size of the solution space of the problem grows exponentially with the increasing number of decision variables, there is an urgent need to develop more effective and efficient search strategies to better explore this vast solution space with limited computational budgets. In recent years, research on scaling up EAs to large-scale problems has attracted significant attention, including both theoretical and practical studies. Existing work on tackling the scalability issue is getting more and more attention in the last few years.
This special session is devoted to highlight the recent advances in EAs for handling large-scale global optimization (LSGO) problems, involving single objective or multiple objectives, unconstrained or constrained, static or dynamic, binary/discrete or real, or mixed decision variables. More specifically, we encourage interested researchers to submit their original and unpublished work on:
1. Theoretical and experimental analysis on the scalability of EAs;
2. Novel approaches and algorithms for scaling up EAs to large-scale optimization problems; this includes but not limited to the following:
- Exploiting problem structure by means of variable interaction analysis and problem decomposition. - Hybridization and memetic algorithms.
- Designing algorithm-specific sampling and variation operators.
- Approximation methods and surrogate modeling.
- Parallel EAs and distributed computing models.
- Using machine learning and data mining to boost the performance of EAs.
- Hybridization between EAs with traditional mathematical approaches.
3. Problem areas such as: large-scale multi-objective problem, problems with overlapping components, resource allocation and the imbalance problem, constrained handling in high-dimensional spaces.
4. Applications of EAs to real-world large-scale optimization problems; e.g., optimization problems in machine learning, healthcare and scheduling etc.
5. Novel test suites that help researches to understand large-scale optimization problems characteristics.
Dr. Mohammad Nabi Omidvar, University of Leeds, UK
Dr. Yuan Sun, Monash University, Australia
Dr. Antonio LaTorre, Universidad Politécnica de Madrid
Dr. Daniel Molina, Granada University, Spain