Tasks scheduling technique using league championship algorithm for makespan minimization in IaaS cloud

Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All algorithms under consideration assumed to be non-preemptive. The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud

A Survey of League Championship Algorithm: Prospects and Challenges

The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.Comment: 10 pages, 2 figures, 2 tables, Indian Journal of Science and Technology, 201

Исследование эффективности популяционного алгоритма лиги чемпионов для задачи глобальной оптимизации

The article objective is to study a new League Championship Algorithm (LCA) algorithm efficiency by its comparing with the efficiency of the Particle Swarm optimization (PSO) algorithm.The article presents a brief description of the terms used in the League Championship algorithm, describes the basic rules of the algorithm, on the basis of which the iterative process for solving the global optimization problem is built.Gives a detailed description of the League Championship algorithm, which comprises a flowchart of the algorithm, as well as a formalization of all its main steps.Depicts an exhaustive description of the software developed to implement the League Championship algorithm to solve global optimization problems.Briefly describes the modified particle swarm algorithm. Presents the values of all free parameters of the algorithm and the algorithm modifications, which make it different from the classical version, as well.The main part of the article shows the results of a great deal of computational experiments using two abovementioned algorithms. All the performance criteria, used for assessment of the algorithms efficiency, are given.Computational experiments were performed using the spherical function, as well as the Rosenbrock, Rastrigin, and Ackley functions. The results of the experiments are summarized in Tables, and also illustrated in Figures. Experiments were performed for the vector dimension of the variable parameters that is equal to 2, 4, 8, 16, 32, and 64.An analysis of the results of computational experiments involves a full assessment of the efficiency of the League Championship algorithm, and also provides an answer about expediency for further algorithm development.It is shown that the League Championship algorithm presented in the article has a high development potential and needs further work for its study.Целью статьи  является исследование эффективности нового алгоритма лиги чемпионов (League Championship Algorithm, LCA) на основе сравнения с эффективностью алгоритма роя частиц (Particle Swarm optimization, PSO).Представлено краткое описание терминов, использующихся в алгоритме лиги чемпионов, изложены основные правила алгоритма, на основе которых строится итерационный процесс решения задачи глобальной оптимизации.Статья содержит подробное описание алгоритма лиги чемпионов, которое включает в себя схематичное представление алгоритма, а также формализованное изложение всех основных его этапов.Приведено исчерпывающее описание разработанного программного обеспечения, которое реализует алгоритм лиги чемпионов для решения задач глобальной оптимизации.Дано краткое описание модифицированного алгоритма роя частиц. Представлены значения всех свободных параметров алгоритма, а также модификации алгоритма, отличающие его от классической версии.Основной частью работы является представление результатов большого числа вычислительных экспериментов, которые проводились с использованием двух указанных алгоритмов. Представлено описание всех критериев эффективности, на основе которых проводилась оценка эффективности алгоритмов.Вычислительные эксперименты выполнены с использованием сферической функции, а также функций Розенброка, Растригина и Экли. Результаты экспериментов сведены в таблицы, а также проиллюстрированы рисунками. Зксперименты проведены для размерности вектора варьируемых параметров равной 2, 4, 8, 16, 32, 64.Выполнен анализ результатов вычислительных экспериментов, который включает в себя полную оценку эффективности алгоритма лиги чемпионов, а также дает ответ на вопрос о целесообразности проведения последующих работ, направленных на развитие алгоритма.Показано, что представленный в работе алгоритм лиги чемпионов имеет высокий потенциал развития и требует проведения дальнейших работ по его изучению

Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

Peer reviewedPublisher PD

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

On a novel hybrid Manta ray foraging optimizer and its application on parameters estimation of lithium-ion battery

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon's test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.Web of Science151art. no. 6

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure