یک الگوریتم کرم شب تاب اصلاح شده برای بهینه سازی حداقل مطلق
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یک الگوریتم کرم شب تاب اصلاح شده برای بهینه سازی حداقل مطلق

عنوان فارسی مقاله: یک الگوریتم کرم شب تاب اصلاح شده برای بهینه سازی حداقل مطلق
عنوان انگلیسی مقاله: A modified firefly algorithm for global minimum optimization
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: هوش مصنوعی، مهندسی الگوریتم ها و محاسبات، مهندسی نرم افزار
کلمات کلیدی فارسی: الگوریتم کرم شب ناب، نیروی جزر و مدی، بهینه سازی، هوش ازدحامی، حداقل مطلق
کلمات کلیدی انگلیسی: Firefly algorithm، Tidal force، Optimization، Swarm intelligence، Global minimum
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2017.10.032
دانشگاه: Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 6/031 در سال 2018
شاخص H_index: 110 در سال 2019
شاخص SJR: 1/216 در سال 2018
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 16
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11308
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Proposed work


4- Comparative study


5- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


The Firefly algorithm is a population-based optimization algorithm. It has become popular in the field of optimization and has been applied to engineering practices. Recent works have failed to address how to find the global minimum because their algorithm was trapped in the local minimum. Also, they were not able to provide a balance between exploration and exploitation. In this paper, the Tidal Force formula has been applied to modify the Firefly algorithm, which describes the effect of a massive body that gravitationally affects another massive body. The proposed algorithm brings a new strategy into the optimization field. It is applied by using exploitation (Tidal Force) and keeping a balance between the exploration and exploitation on function suitability. Plate shaped, Steep Ridges, Unimodal and Multimodal benchmark functions were used to compare experimental results. The study findings indicate that the Tidal Force Firefly algorithm outperforms the other existing modified Firefly algorithms.


Introduction


Population-based optimization techniques have become widespread in the last two decades. Optimization problems are examined in a variety of fields, which consist of highly nonlinear, multimodal, multidimensional, and differentiable functions. However, traditional optimization techniques have not been able to solve these problems. The population-based techniques, with their own robustness and flexible behavior in solving optimization problems, bring novel insights in order to solve the problems instead of using traditional optimization techniques. The PSO algorithm is inspired by birds’ behavior [1], which defines that all birds move towards the best bird. The PSO works with two populations, such as best position and current positions. Diversity solutions are one of the advantages of the PSO, which performs better than the single point algorithms. An ant colony is another popular algorithm that was designed for optimization problems. The algorithm is based on the behavior of ants and was proposed by Dorigo. Ants search for a best path solution between their colony and a food source. Each ant randomly moves towards a destination ant. The paths are followed by ants, based on the probability of pheromones [2]. Differential evolution based on individual’s differences (called the DE algorithm) is similar to the GA algorithm that defines specified crossover, mutation and selection [3]. It computes parallels and takes the best result in a ∗ Corresponding author. E-mail addresses: arefyelghi@ktu.edu.tr (A. Yelghi), ckose@ktu.edu.tr (C. Köse). few dimensions. The Harmony Search (HS) algorithm was modeled by taking inspiration from the improvisation of musicians [4]. The algorithm uses musical Pitch Range, Harmony, Aesthetics, Practice and experience in the algorithm, which links to decision variables, iteration concepts and so on. The harmony (solution) is produced randomly and checks with stored solutions to place better solutions in their place.

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