Evolutionary Computation for Dynamic Combinatorial Optimisation Problems
Evolutionary Computation (EC) encapsulates a class of stochastic optimisation algorithms, which are inspired by principles from natural and biological evolution. EC methods have been widely used for combinatorial optimisation problems (COPs) in many fields. Traditionally, EC methods have been applied for solving static COPs. However, many real world COPs are dynamic COPs (DCOPs), which are subject to changes over time due to many factors. DCOPs pose serious challenges to traditional EC methods since they cannot adapt well to a changing environment once converged. DCOPs have attracted a growing interest from the EC community in recent years due to the importance in the real-world applications of EC.
This talk will first briefly introduce the concept of DCOPs and several benchmark DCOPs for testing EC methods, then review the main approaches developed to enhance EC methods for DCOPs, and describe several detailed approaches developed for EC methods for DCOPs. This talk will then present some case studies on EC for DCOPs in the real world. Finally, some conclusions will be made based on the work presented and the future work on EC for DCOPs will be briefly discussed.
Shengxiang Yang is Professor of Computational Intelligence (CI) and Director of the Centre for Computational Intelligence at De Montfort University. He has worked at King's College London, University of Leicester, and Brunel University and has worked extensively for over 15 years in the areas of CI methods, including Evolutionary Computation and Artificial Neural Networks, and their applications for real-world problems. He has over 160 publications in these domains. His work has been supported by UK research councils and industry partners, with a total funding of over £1M to him as the PI.