Modern Optimization and Search Algorithms: From Classical Approaches to Nature-Inspired Techniques


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Authors

  • Güray TONGUÇ Akdeniz Üniversitesi

DOI:

https://doi.org/10.70447/ktve.2561

Keywords:

Optimizasyon, Meta-sezgisel algoritmalar, Doğa-esinli yaklaşımlar, Arama algoritmaları, Yapay zeka

Abstract

This study focuses on the fundamental principles, methods, and application areas of optimization and search algorithms. The basic concepts of optimization, such as the definition, decision variables, objective function, and constraints, are discussed, and the characteristics of analytical, heuristic, and meta-heuristic methods are detailed. It emphasizes that nature-inspired meta-heuristic methods provide quick and effective solutions for complex problems. The Dijkstra, Bellman-Ford, A*, Firefly, Ant Colony, and Wolf Colony algorithms are examined, and their effectiveness and usage examples in various application areas (such as engineering, artificial intelligence, data analytics, and robotics) are discussed. The study highlights the importance of optimization techniques in developing solutions for complex problems.

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Published

2024-11-28

How to Cite

TONGUÇ, G. (2024). Modern Optimization and Search Algorithms: From Classical Approaches to Nature-Inspired Techniques. Journal of Quantum Technologies and Informatics Research, 2(3). https://doi.org/10.70447/ktve.2561

Issue

Vol. 2 No. 3 (2024): Quantum Technologies and Informatics Research (JQTAIR)

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Articles

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Copyright (c) 2024 Güray TONGUÇ

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