A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA

A New Fusion of Salp Swarm with Sine Cosine for Optimization of Non-linear Functions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the Salp Swarm Algorithm with Sine Cosine Algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated by using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in Salp Swarm Optimizer Algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on twenty-two standard mathematical optimization functions and three applications namely the three-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others

Performance Analysis of Metaheuristic Optimization Algorithms in Estimating the Interfacial Heat Transfer Coefficient on Directional Solidification

In this paper is proposed an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon. It was considered an upward directional solidification system for Al-7wt.% Si alloy and, for IHTC model, a exponential time function. All thermophysical properties of the alloy were considered constant. Scheil Rule was used as segregation model ahead phase-transformation interface. Optimization results from Markov Chain Monte Carlo method (MCMC) were considered as reference. Based on average, quantiles 95% and 5%, kurtosis, average iterations and absolute errors of the metaheuristic methods, in relation to MCMC results, the Flower Pollination Algorithm (FPA) and Moth-Flame Optimization (MFO) presented the most appropriate results, outperforming the other methods in this particular phenomenon, based on these metrics. The regions with the most probable values for parameters in IHTC time function were also determined.Comment: 27 pages, 7 figures, 4 tables, 67 references cited, preprin

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

An enhanced brain storm sine cosine algorithm for global optimization problems

The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability

Multi-Objective Optimization in Metabolomics/Computational Intelligence

The development of reliable computational models for detecting non-linear patterns encased in throughput datasets and characterizing them into phenotypic classes has been of particular interest and comprises dynamic studies in metabolomics and other disciplines that are encompassed within the omics science. Some of the clinical conditions that have been associated with these studies include metabotypes in cancer, in ammatory bowel disease (IBD), asthma, diabetes, traumatic brain injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few. The traction in this domain is attributable to the advancements in the procedures involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of a wide abundance of datasets. Throughput datasets generated by modern 1H NMR spectrometers are often characterized with features that are uninformative, redundant and inherently correlated. This renders it di cult for conventional multivariate analysis techniques to e ciently capture important signals and patterns. Therefore, the work covered in this research thesis provides novel alternative techniques to address the limitations of current analytical pipelines. This work delineates 13 variants of population-based nature inspired metaheuristic optimization algorithms which were further developed in this thesis as wrapper-based feature selection optimizers. The optimizers were then evaluated and benchmarked against each other through numerical experiments. Large-scale 1H NMR-linked datasets emerging from three disease studies were employed for the evaluations. The rst is a study in patients diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked by a distinctive facial appearance, learning disabilities, and gigantism culminating in tall stature and macrocephaly, also referred to as cerebral gigantism. Another study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative condition marked by intracellular accrual of cholesterol and complex lipids including sphingolipids and phospholipids in the endosomal/lysosomal system. The third study involved sore throat investigation in human (also known as `pharyngitis'); an acute infection of the upper respiratory tract that a ects the respiratory mucosa of the throat. In all three cases, samples from pathologically-con rmed cohorts with corresponding controls were acquired, and metabolomics investigations were performed using 1H NMR technique. Thereafter, computational optimizations were conducted on all three high-dimensional datasets that were generated from the disease studies outlined, so that key biomarkers and most e cient optimizers were identi ed in each study. The clinical and biochemical signi cance of the results arising from this work were discussed and highlighted

Learning Optimal Time Series Combination and Pre-Processing by Smart Joins

In industrial applications of data science and machine learning, most of the steps of a typical pipeline focus on optimizing measures of model fitness to the available data. Data preprocessing, instead, is often ad-hoc, and not based on the optimization of quantitative measures. This paper proposes the use of optimization in the preprocessing step, specifically studying a time series joining methodology, and introduces an error function to measure the adequateness of the joining. Experiments show how the method allows monitoring preprocessing errors for different time slices, indicating when a retraining of the preprocessing may be needed. Thus, this contribution helps quantifying the implications of data preprocessing on the result of data analysis and machine learning methods. The methodology is applied to two case studies: synthetic simulation data with controlled distortions, and a real scenario of an industrial process.This research has been partially funded by the 3KIA project (ELKARTEK, Basque Government)

Métodos comparativos para la solución óptima del flujo de energía en redes de distribución considerando generadores distribuidos: metaheurística vs. optimización convexa

Objective: This article presents an analysis of different optimization methodologies, which aims to make an objective comparison between metaheuristic and convex optimization methods in distribution networks, focusing on the inclusion of distributed generation (DG). The MATLAB software is used as a tool for implementation and obtaining results. The objective was to determine the optimal size of the DGs to be integrated into the networks, with the purpose of reducing the active power losses (objective function). Methodology: Based on the specialized literature, the methodologies are selected, and the bases and conditions for the implementation of the optimization techniques are determined. In the case of second-order cone programming (SOCP), the relaxation of the nonlinear optimal power flow (OPF) problem is performed in order to use convex optimization. Then, the structures of each technique are established and applied in the MATLAB software. Due to the iterative nature of metaheuristic methods, the data corresponding to 100 compilations for each algorithm are collected. Finally, by means of a statistical analysis, the optimal solutions for the objective function in each methodology are determined, and, with these results, the different methods applied to the networks are compared. Results: By analyzing 33- and 69-node systems, it is demonstrated that metaheuristic methods are able to effectively size DGs in distribution systems and yield good results that are similar and comparable to SOCP regarding the OPF problem. Genetic algorithms (GA) showed the best results for the studied implementation, even surpassing the SOCP. Conclusions: Metaheuristic methods proved to be algorithms with a high computational efficiency and are suitable for real-time applications if implemented in distribution systems with well-defined conditions. These techniques provide innovative ideas because they are not rigid algorithms, which makes them very versatile methods that can be adapted to any combinatorial optimization problem and software, yielding results even at the convex optimization level.Objetivo: Este artículo presenta un análisis de diferentes metodologías de optimización, cuyo fin es realizar una comparación objetiva entre métodos de optimización metaheurística y convexa en redes de distribución con énfasis en la inclusión de generación distribuida (DG). Se utiliza el software MATLAB como herramienta para la implementación y la obtención de resultados. El objetivo es determinar el tamaño óptimo de las DG a integrar en las redes, con el fin de reducir las pérdidas de potencia activa (función objetivo). Metodología: A partir de la literatura especializada, se seleccionan las metodologías y se determinan las bases y condiciones para la implementación de las técnicas de optimización. En el caso de la programación cónica de segundo orden (SOCP), se realiza la relajación del problema de flujo de potencia óptimo (OPF) no lineal para utilizar optimización convexa. Luego, las estructuras de cada técnica se establecen y aplican en el software MATLAB. Debido al carácter iterativo de los métodos metaheurísticos, se recolectan los datos correspondientes a 100 compilaciones para cada algoritmo. Finalmente, mediante un análisis estadístico, se determinan las soluciones óptimas para la función objetivo en cada metodología y, con estos resultados, se comparan los diferentes métodos aplicados a las redes. Resultados: A partir del análisis de sistemas de 33 y 69 nodos, se demuestra que los métodos metaheurísticos son capaces de dimensionar DGs manera efectiva en sistemas de distribución y dan buenos resultados, similares y comparables a la SOCP en el problema OPF. El algoritmo genético (GA) mostró los mejores resultados para la implementación realizada, superando incluso a la SOCP. Conclusiones: Los métodos metaheurísticos demostraron ser algoritmos de alta eficiencia computacional y son adecuados para aplicaciones en tiempo real si se implementan en sistemas de distribución con condiciones correctamente definidas. Estas técnicas aportan ideas innovadoras porque no son algoritmos rígidos, lo que las convierte en métodos muy versátiles que pueden adaptarse a cualquier problema de optimización combinatoria y a cualquier software, dando resultados incluso a nivel de optimización convexa

DTCWTASODCNN: DTCWT based Weighted Fusion Model for Multimodal Medical Image Quality Improvement with ASO Technique & DCNN

Medical image fusion approaches are sub-categorized as single-mode as well as multimodal fusion strategies. The limitations of single-mode fusion approaches can be resolved by introducing a multimodal fusion approach. Multimodal medical image fusion approach is formed by integrating two or more medical images of similar or dissimilar modalities aims to enhance the image quality and to preserve the image information. Hence, this paper introduced a new way to meld multimodal medical images via utilizing developed weighted fusion model relied on Dual Tree Complex Wavelet Transform (DTCWT) for fusing the multimodal medical image. Here, the two medical images are considered for image fusion process and we have implied DTCWT to the medical images for generating four sub-bands partition of the source medical images. The Renyientropy-based weighted fusion model is used to combine the weighted coefficient of DTCWT of images. The final fusion process is carried out using Atom Search Sine Cosine Algorithm (ASSCA)-based Deep Convolutional Neural Network (DCNN). Moreover, the simulation work output demonstrated for developed fusion model gained the superior outcomes relied on key indicators named as Mutual Information i.e. MI, Peak Signal to Noise Ratio abbreviated as PSNR as well as Root Mean Square Error, in short RMSE with the values of 1.554, 40.45 dB as well as 5.554, correspondingly