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Study on Some Computing Approaches for Problem Solving in Optimization
Optimization is a fundamental component of mathematical and computational study that o separates the best option from a variety of feasible options. Numerous disciplines, including operations research, economics, and engineering, can benefit from this method. Finding an objective function's highest or lowest value is the primary goal of optimization. Several restrictions or limitations are frequently associated with this function, which are important to consider. Optimization facilitates the process of making the best decisions and attaining the greatest results in a variety of applications by navigating these restrictions.
This book aims to increase efficiency and effectiveness in a variety of applications by investigating sophisticated optimization approaches. Key approaches examined include swarm intelligence algorithms, like the Salp Swarm Algorithm” and Sine Cosine Algorithm. The study shows how these methods efficiently achieve an equilibrium between exploration and exploitation inside search spaces through an evaluation using benchmark datasets and real-world challenges. Furthermore, SSA and SCA are compared using benchmark functions of convergence curves that are unimodal and multimodal, respectively, in order to figure out which algorithm functions better in everyday circumstances. The findings indicate that with respect to reliability of classification, the Salp Swarm Algorithm” achieves better than the Sine Cosine Algorithm. Significant improvements in performance measures are demonstrated by these studies, highlighting the potential applications of these optimization techniques in several fields, including as engineering design, machine learning, and data analysis. All things considered, this work promotes towards the development of reliable and flexible optimization strategies and offers valuable data for both practical applications and next research projects.
This book aims to increase efficiency and effectiveness in a variety of applications by investigating sophisticated optimization approaches. Key approaches examined include swarm intelligence algorithms, like the Salp Swarm Algorithm” and Sine Cosine Algorithm. The study shows how these methods efficiently achieve an equilibrium between exploration and exploitation inside search spaces through an evaluation using benchmark datasets and real-world challenges. Furthermore, SSA and SCA are compared using benchmark functions of convergence curves that are unimodal and multimodal, respectively, in order to figure out which algorithm functions better in everyday circumstances. The findings indicate that with respect to reliability of classification, the Salp Swarm Algorithm” achieves better than the Sine Cosine Algorithm. Significant improvements in performance measures are demonstrated by these studies, highlighting the potential applications of these optimization techniques in several fields, including as engineering design, machine learning, and data analysis. All things considered, this work promotes towards the development of reliable and flexible optimization strategies and offers valuable data for both practical applications and next research projects.
104 pages, Kindle Edition
Published June 27, 2024
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