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Cooperative Co-Evolution and MapReduce: A Review and New Insights for Large-Scale Optimisation

Cooperative Co-Evolution and MapReduce: A Review and New Insights for Large-Scale Optimisation
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Author(s): A. N. M. Bazlur Rashid (Edith Cowan University, Australia) and Tonmoy Choudhury (Edith Cowan University, Australia)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 34
Source title: International Journal of Information Technology Project Management (IJITPM)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/IJITPM.2021010102

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Abstract

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.

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