期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2020
卷号：20
期号：8
页码：30-68
出版社：International Journal of Computer Science and Network Security
摘要：There is a shred of ample evidence that optimization is an enormous field that pervades essentially every aspect of our day-to-day life ranging from academic and engineering fields, going to industrial and agricultural segments, passing through social domains, and ending with commercial and business sectors. Evidently, the philosophy of optimization has emerged out of the utmost need for finding the best available solution among a set of candidate ones, without which our life will lose its vitality.
Over the last few decades, a worthy amount of interest has been focused on finding solutions for a wide range of intractable optimization problems by scientists and researchers from diversified domains not only for academic and research objectives but also due to the existence of a wide variety of real-life applications. They indeed see the remarkable resemblance between the swarms, for instance, and the behavior of a human in solving problems and trying to come up with new goal-oriented operating methods to tackle many important real-world problems. Nature Inspired Computing (NIC), as its name implies, is the fusion of nature, by itself, and Artificial Intelligence (AI) to solve various global optimization problems. Furthermore, swarm optimization is considered as the most representative of these nature-inspired algorithms. Motivated by applying natural phenomena to metaheuristics and trying to simulate the harmonious behaviors of creatures in solving problems particularly the joint hunting behavior of the sea lions, the aim of the research work reported in this paper is twofold. On the one hand, many theoretical and practical aspects of heuristic and metaheuristic approaches, from classical to novel approaches, are discussed and covered. On the other hand, this nature-inspired paper addresses a pioneer metaheuristic optimization algorithm for determining the optimal solution for the Maximum Flow Problem (MFP). To be more precise, this paper elaborates on using the Sea Lion Optimization (SLnO) Algorithm for solving the Maximum Flow Problem (MFP), hence the name “SLnO-MFP”.
After the proposed solution SLnO-MFP algorithm is analyzed and the experimental tests are conducted on various real-case datasets, the reported practical results are represented, discussed, and compared using the same datasets with other algorithms, including Whale Optimization algorithm (WOA) and Ford-Fulkerson (FF) algorithm, which have been used to solve the same problem of interest. As the accomplishment achieved in this valuable research is efficient and robust, the proposed algorithm is proved to be a senior-level alternative to the optimization problem and, in turn, can be efficiently used to solve various optimization problems having a fairly large-scale data such as the underlying problem (i.e. MFP).
关键词：Artificial Intelligence (AI); Global optimization; Maximum Flow Problem (MFP); Metaheuristic Algorithms; Optimization; Sea Lion Optimization (SLnO) Algorithm; Swarm Intelligence.
