The Application of Ant Colony Optimization

The book aims to provide practical help for researchers, and industrial specialists who are interested in using the new science and technology to improve their knowledge and experience in the field of optimization.

Optimization problems are very important in the field of both scientific and industrial. Some real-life examples of these optimization problems are timetable scheduling, nursing time distribution scheduling, train scheduling, capacity planning, vehicle routing problems, group-shop scheduling problem, portfolio optimization, etc. Many optimizations algorithms are developed for this reason. Ant Colony Optimization (ACO) is one of them. ACO is a probabilistic technique for finding optimal paths. In computer science and researches, the ACO algorithm is used for solving different computational problems.

There are many optimization problems where the ACO can be applied for finding the optimal solution. Some of them are Image processing, Capacitated vehicles routing, Stochastic vehicles routing, Vehicle routing with pick-up and delivery, Connectionless network routing, Data mining, Group-shop scheduling, Distributed information retrieval, Nursing time distribution, Permutation flow shop problem, Frequency assignment problem, Redundancy allocation problem, and Electricity network design.

The Objective of the book is to provide a concise overview of the state of the art of ACO for industrial researchers. They will value a book that helps them position the emerging capabilities of ACO in their businesses and provide an assessment of where and how these new capabilities can help to optimize the end to end operations of their enterprises.

1. Image Processing by Ant Colony Optimization (ACO)
2. Vehicles Routing by Ant Colony Optimization (ACO)
3. Connectionless network routing by Ant Colony Optimization (ACO)
4. Data mining by Ant Colony Optimization (ACO)
5. Distributed information retrieval by Ant Colony Optimization (ACO)
6. Energy and electricity network design by Ant Colony Optimization (ACO)

Dr. Ali Soofastaei

Biography 

Dr. Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author.

He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology.

Dr. Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from his research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia.

He has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams.

Dr. Soofastaei is working actively with some prestigious global publishers same as Mc Graw-Hill Education, Intech Open, Springer, and CRC Press as an author and academic editor.

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