Advanced Metaheuristics for Optimization

dc.contributor.advisor	Fazekas, Gabor
dc.contributor.author	Sabagh Nejad, Anahita
dc.contributor.department	Informatikai tudományok doktori iskola	hu
dc.contributor.submitterdep	Informatikai Kar
dc.date.accessioned	2024-01-31T10:01:01Z
dc.date.available	2024-01-31T10:01:01Z
dc.date.created	2024/01/29
dc.date.defended	2024-01-29
dc.description.abstract	The main purpose of this dissertation is to introduce two new advanced methods of solving a Traveling salesman problem (TSP) using metaheuristics. Solving TSP is important as it is an NP-hard and can’t be solved in a polynomial time. In my new methods, I applied k-means to cluster the data into smaller parts and I used the Whale Optimization algorithm (WOA) as a bio-inspired algorithm. I merged TSP with K-means and WOA, and I assigned a number for thresholding (T- value) to decide the maximum number of cities that can be placed in each cluster. This way the fitness function and timing of solving TSP improved. The two methods have close pseudocodes. There is a third model as well that is proposed for future works.
dc.format.extent	174
dc.identifier.uri	https://hdl.handle.net/2437/365770
dc.language.iso	en
dc.subject	Metaheuristics
dc.subject	TSP
dc.subject	K-means
dc.subject	Whale algorithm
dc.subject	Optimization
dc.subject.discipline	Informatikai tudományok	hu
dc.subject.sciencefield	Műszaki tudományok	hu
dc.title	Advanced Metaheuristics for Optimization
dc.title.translated	Erweiterte Metaheuristik zur Optimierung

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