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Nodari Vakhania

Universidad Autónoma del Estado de Morelos, Mexico

Nodari Vakhania graduated with honors from the Faculty of Applied Mathematics and Cybernetics, Tbilisi State University, Georgia, in 1983. In 1989, he entered the Ph.D. program in Computer Science at the Russian Academy of Sciences, Moscow, and obtained a degree in Mathematical Cybernetics in 1991. In 1992, he was a postdoctoral fellow at the Russian Academy of Sciences and had a short-term visiting position at the University of Saarbrucken, Germany. In 1995, he became a professor at the Centro de Investigación en Ciencias, State University of Morelos, Mexico. He also has an honorary position at the Institute of Computational Mathematics of the Georgian Academy of Sciences, where he received a doctoral degree in Mathematical Cybernetics in 2004. His research interests include the design and analysis of algorithms, discrete optimization, computational complexity, and scheduling theory. He is an author of nearly 100 refereed research papers including more than 60 publications in highly ranked international journals. He has also worked with different scientific committees, including those at the Mexican Science Foundation CONACyT. He is an editorial board member and a referee for several international scientific journals. He has obtained research grants and honors in Germany, France, the Netherlands, United States, Russia, and Mexico, and has given more than forty invited talks throughout the world.

2books edited

2chapters authored

Latest work with IntechOpen by Nodari Vakhania

Optimisation is one of the unavoidable key subjects in engineering and other real-world problems, which attracts researchers’ and practitioners’ attention for decades. On the other hand, computational algorithms nowadays play a definitive role in most real-life applications, from mobile phones to supercomputers, Internet servers, manufacturing, etc. An intelligent method for the enumeration of feasible solutions may lead to efficient computational algorithms. Swarm intelligence emerges as a rather new and novel of field computational intelligence that turned into a hot spot in optimization studies last two decades. This book brings together a number of research articles within the intersection of these two prominent subjects, which introduces techniques and approaches in detail and demonstrates how optimisation problems can be solved with heuristic and swarm intelligence approaches. It contains a few contributions on Particle Swarm Optimisation (PSO) area, which is one of renown swarm optimisation approaches that will shed light to issues around optimisation with swarm intelligence to guide junior researchers with implementation details provided.

Optimisation Algorithms and Swarm Intelligence

Optimisation Algorithms and Swarm Intelligence

Edited by Nodari Vakhania and Mehmet Emin Aydin

Books edited

Multicriteria Optimization

Multicriteria Optimization

Edited by Nodari Vakhania

Chapters authored

Efficient Heuristics for Scheduling with Release and Delivery Times

By Nodari Vakhania

In this chapter, we describe efficient heuristics for scheduling jobs with release and delivery times with the objective to minimize the maximum job completion time. These heuristics are essentially based on a commonly used scheduling theory in Jackson’s extended heuristic. We present basic structural properties of the solutions delivered by Jackson’s heuristic and then illustrate how one can exploit them to build efficient heuristics.

Part of the book: Heuristics and Hyper-Heuristics

A Brief Look at Multi-Criteria Problems: Multi-Threshold Optimization versus Pareto-Optimization

By Nodari Vakhania and Frank Werner

Multi-objective optimization problems are important as they arise in many practical circumstances. In such problems, there is no general notion of optimality, as there are different objective criteria which can be contradictory. In practice, often there is no unique optimality criterion for measuring the solution quality. The latter is rather determined by the value of the solution for each objective criterion. In fact, a practitioner seeks for a solution that has an acceptable value of each of the objective functions and, in practice, there may be different tolerances to the quality of the delivered solution for different objective functions: for some objective criteria, solutions that are far away from an optimal one can be acceptable. Traditional Pareto-optimality approach aims to create all non-dominated feasible solutions in respect to all the optimality criteria. This often requires an inadmissible time. Besides, it is not evident how to choose an appropriate solution from the Pareto-optimal set of feasible solutions, which can be very large. Here we propose a new approach and call it multi-threshold optimization setting that takes into account different requirements for different objective criteria and so is more flexible and can often be solved in a more efficient way.

Part of the book: Multicriteria Optimization

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