Implementation of the Sine Cosine Algorithm and its variants for solving the tension compression spring design problem

Ο αλγόριθμος ημιτόνου και συνημίτονου εφευρέθηκε από τον Mirjalili το 2016. Χρησιμοποιεί τις συναρτήσεις ημιτόνου και συνημίτονου για να επιλύσει ένα μεγάλο εύρος προβλημάτων βελτιστοποίησης. Ανήκει σε μια κατηγορία μεταευρετικών διαδικασιών, που περιλαμβάνει στρατηγικές βασισμένες σε πληθυσμό, για επίτευξη βέλτιστου αποτελέσματος μιμούμενο φαινόμενα στη φύση. Έπειτα, έγινε εμβάθυνση σε ένα μεγάλο εύρος παραλλαγών του αλγορίθμου. Ειδικότερα, ασαφής, χαοτικός, βασισμένος σε αντίθετη μάθηση, άπληστος levy, προσαρμοστικός και πολλαπλών στόχων aquila είναι κάποιες από τις μεταλλάξεις του αλγορίθμου που βασίστηκε η εργασία και βελτιώνουν την απόδοση του σημαντικά. Η εργασία είναι στηριγμένη τόσο στο θεωρητικό όσο και στο πρακτικό κομμάτι του αλγορίθμου καθώς επιδιώχθηκε να ελεγχτεί η αποδοτικότητα του με πολλαπλές συναρτήσεις κριτηρίου. Επεκτείνεται η έρευνα στο αντικείμενο επιλύοντας ένα ευρέως γνωστό πρόβλημα μηχανικής, του σχεδιασμού τάσης ελατηρίου. Παρατηρείται ότι ο αλγόριθμος έχει εφαρμογή σε ποικιλία μηχανικών, μαθηματικών και ιατρικών θεμάτων. Είναι αντιληπτό ότι βρίσκει λύση εκεί που άλλες ντετερμινιστικές διαδικασίες δεν μπορούν να εφαρμοστούν. Πολλές παραλλαγές του αλγορίθμου ημιτόνου συνημίτονου έχουν εμφανιστεί για να ισορροπήσουν τις αδυναμίες του. Τέλος, παρουσιάζονται διαγράμματα για υπάρχει καλύτερη αντίληψη της απόδοσης του SCA.The Sine and Cosine Algorithm was created by Seyedali Mirjalili in 2015. It uses sine and cosine to solve various optimisation problems precisely. It belongs to a category of metaheuristics, which includes population-based strategies for obtaining the optimal result by mimicking natural phenomena. This thesis elaborates on a wide variety of its mutants. Specifically, fuzzy, chaotic, opposite-based-learning, greedy levy flight and adaptive multi-objective aquila are some of the variants the work focuses on. This work is based on both theoretical and practical aspects of the algorithm. First, tests of efficiency were pursued on multiple benchmark functions. The research on the topic was expanded by the solution of a widely known engineering problem, the tension/compression spring design. It can be observed that the algorithm has relevance to various engineering, mathematical and medical issues when other deterministic ways fail. Many variants of the procedure were introduced to balance its weaknesses. Finally, diagrams are presented to improve our understanding of the SCA’s accuracy

Optimal PID controller for the DC-DC buck converter using the improved sine cosine algorithm

This paper presents an improved Sine Cosine Algorithm (ISCA) towards optimization of a DC-DC buck converter with employment of the proportional-integral-derivative (PID) controller. Limitations of the conventional Sine Cosine Algorithm (SCA) was hereby overcome through two separate alterations which pioneered a synergized employment of nonlinear equation to instrumental mechanism in revising the average location. Primary alteration tackled the issue of local optima by proposed instrumental function towards revision of average location. Secondary alteration then coordinated disproportional exploration and exploitation phases of the conventional SCA by application of a nonlinear equation against the algorithm's decreasing position-updated mechanism. Robustness of the proposed ISCA-PID approach was studied against preceding algorithm-based PID for DC-DC buck converter with respect to their step response, statistics regarding the analyzed objective function, time-domain integral-error performance, reaction to frequency, and resistance to disturbance and parametric uncertainties. Generated findings subsequently uncovered overshadowing efficacy of the proposed method over its algorithmic predecessors towards exceptionally enhanced transitory response of the DC-DC buck converter

Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.This research was supported by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 cofinanced by FEDER funds and the Ministry of Science and Innovation and the Research State Agency under Grant PID2020-120213RB-I00 cofinanced by FEDER funds

Learning-Based Arabic Word Spotting Using a Hierarchical Classifier

The effective retrieval of information from scanned and written documents is becoming essential with the increasing amounts of digitized documents, and therefore developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Such systems have been implemented for Latin-based and Chinese languages, while few of them have been implemented for Arabic handwriting. The fact that Arabic writing is cursive by nature and unconstrained, with no clear white space between words, makes the processing of Arabic handwritten documents a more challenging problem. In this thesis, the design and implementation of a learning-based Arabic handwritten word spotting system is presented. This incorporates the aspects of text line extraction, handwritten word recognition, partial segmentation of words, word spotting and finally validation of the spotted words. The Arabic text line is more unconstrained than that of other scripts, essentially since it also includes small connected components such as dots and diacritics that are usually located between lines. Thus, a robust method to extract text lines that takes into consideration the challenges in the Arabic handwriting is proposed. The method is evaluated on two Arabic handwritten documents databases, and the results are compared with those of two other methods for text line extraction. The results show that the proposed method is effective, and compares favorably with the other methods. Word spotting is an automatic process to search for words within a document. Applying this process to handwritten Arabic documents is challenging due to the absence of a clear space between handwritten words. To address this problem, an effective learning-based method for Arabic handwritten word spotting is proposed and presented in this thesis. For this process, sub-words or pieces of Arabic words form the basic components of the search process, and a hierarchical classifier is implemented to integrate statistical language models with the segmentation of an Arabic text line into sub-words. The holistic and analytical paradigms (for word recognition and spotting) are studied, and verification models based on combining these two paradigms have been proposed and implemented to refine the outcomes of the analytical classifier that spots words. Finally, a series of evaluation and testing experiments have been conducted to evaluate the effectiveness of the proposed systems, and these show that promising results have been obtained

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Collected Papers (Neutrosophics and other topics), Volume XIV

This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar