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Mehmet Emin Aydin

University of the West of England, United Kingdom

Mehmet Emin Aydin is a Senior Lecturer in Computer Science at, the University of the West of England. He obtained a BSc, MA, and Ph.D. from Istanbul Technical University, Istanbul University, and Sakarya University, Turkey, respectively. His research interests include machine learning, intelligent agents and multi-agent systems, metaheuristics, swarm intelligence, resource planning, scheduling and optimization, and combinatorial optimization. He has served as guest editor for special issues of peer-reviewed international journals. In addition to being a member of the advisory committees of many international conferences, Dr. Aydin is an editorial board member for various journals. He is currently a fellow of the Higher Education Academy, a member of Engineering and Physical Sciences Research Council (EPSRC) College, UK, and a senior member of the Association of Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers (IEEE).

1books edited

2chapters authored

Chapters authored

Simulated Annealing and Multiuser Scheduling in Mobile Communication Networks

By Raymond Kwan, M. E. Aydin and Cyril Leung

Part of the book: Simulated Annealing

Multi Strategy Search with Crow Search Algorithm

By Rafet Durgut and Mehmet Emin Aydin

Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.

Part of the book: Optimisation Algorithms and Swarm Intelligence

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Instituto Federal Fluminense , Brazil

Fran Sergio Lobato

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Luis San José-Revuelta

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Yiqiang Sheng

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Atsushi Takahashi

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Ardeshir Bahreininejad

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Ali Sadollah

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Elaine Gomes De Assis

Patrick Guerin

University of Nantes , France