Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/901
Title: A novel local search method for LSGO with golden ratio and dynamic search step
Authors: Koçer, Havva Gül
Uymaz, Sait Ali
Keywords: Large-Scale Global Optimization
Local Search
Golden Ratio
Memetic Algorithm
Cec'2013 Lsgo Benchmark
Evolutionary Algorithms
Differential Evolution
Optimization
Issue Date: 2021
Publisher: SPRINGER
Abstract: Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm's performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC'2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions.
URI: https://doi.org/10.1007/s00500-020-05284-x
https://hdl.handle.net/20.500.13091/901
ISSN: 1432-7643
1433-7479
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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