Title

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

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

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

Not free_to_read

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