Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye
Tezin Onay Tarihi: 2019
Tezin Dili: İngilizce
Öğrenci: BAHADUR ALIZADA
Danışman: Mustafa Danacı
Özet:
The difficulty in solving engineering problems creates difficulties in the selection of the methods to be used. Nature-inspired herd intelligence-based meta-heuristic optimization techniques have recently become the most popular algorithms for solving such problems. In this thesis, two new hybrid algorithm models have been developed to adapt to various problems. The developed models were adapted to 23 Benchmark test problems in the literature and compared with meta-heuristic algorithms. The aim of the algorithms is to balance the optimization processes of exploration and exploitation. In the development of a meta-heuristic algorithm, it is very difficult to achieve a balance due to its stochastic structure. In this study, HSKKAO hybrid model of Ant Lion developed by using Sine Cosine Algorithm (SCA) has improved the missing aspects of both algorithms and achieved very successful results. Similarly, the new hybrid model improved by Multi-Verse Optimization (MVO) on the Sine Cosine Whale Optimization Algorithm (SCBOA) hybrid model, which is available in the literature, has increased the success of test problems. In the literature Benchmark test results of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Cuckoo Search (CS), Firefly Algorithm (FA), States of Matter Search (SMS), Gravity Search Algorithm (GSA) and Fast Evolutionary Programming (FEP) were compared with the results of the hybrid models, and very successful and competitive results were obtained. Keyword: Benchmark, meta-heuristic algorithms, Sinus Cosine Algorithm, The Ant Lion Optimization, Whale Optimization Algorithm, Multi-Verse Optimization