Cooperative co-evolution and mapreduce: A review and new insights for large-scale optimisation
Document Type
Journal Article
Publication Title
International Journal of Information Technology Project Management
Volume
12
Issue
1
First Page
29
Last Page
62
Publisher
IGI Global
School
School of Science / Graduate Research School / School of Business and Law
RAS ID
38847
Abstract
© 2021 IGI Global. All rights reserved. Real-word large-scale optimisation problems often result in local optima due to their large search space and complex objective function. Hence, traditional evolutionary algorithms (EAs) are not suitable for these problems. Distributed EA, such as a cooperative co-evolutionary algorithm (CCEA), can solve these problems efficiently. It can decompose a large-scale problem into smaller sub-problems and evolve them independently. Further, the CCEA population diversity avoids local optima. Besides, MapReduce, an open-source platform, provides a ready-to-use distributed, scalable, and fault-tolerant infrastructure to parallelise the developed algorithm using the map and reduce features. The CCEA can be distributed and executed in parallel using the MapReduce model to solve large-scale optimisations in less computing time. The effectiveness of CCEA, together with the MapReduce, has been proven in the literature for large-scale optimisations. This article presents the cooperative co-evolution, MapReduce model, and associated techniques suitable for large-scale optimisation problems.
DOI
10.4018/IJITPM.2021010102
Access Rights
subscription content
Comments
Rashid, A. N. M. B., & Choudhury, T. (2021). Cooperative co-evolution and mapreduce: A review and new insights for large-scale optimisation. International Journal of Information Technology Project Management (IJITPM), 12(1), 29-62. https://doi.org/10.4018/IJITPM.2021010102