Bat Algorithm: Literature Review and Applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.Comment: 10 page

Global convergence analysis of the bat algorithm using a markovian framework and dynamical system theory

The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions

Social welfare maximization based optimal energy and reactive power dispatch using ant lion optimization algorithm

In this paper an optimal energy and reactive power dispatch problem is solved by using the ant lion optimization (ALO) algorithm by considering the total cost minimization and social welfare maximization (SWM) objectives. Two different market models are proposed in this work, i.e., conventional/sequential market clearing and the proposed/simultaneous market clearing. In each market model, two objectives, i.e., total cost minimization and SWM are considered. The conventional social welfare (SW) consists the benefit function of consumers and the cost function of active power generation. In this paper, the conventional SW is modified by including the reactive power cost function. The reactive power cost calculation is exactly same as that in the conventional practice. The most important difference is that instead of doing cost calculation in post-facto manner as in conventional practice, simultaneous approach is proposed in this work. The scientificity and suitability of the proposed simultaneous active and reactive power methodology has been examined on standard IEEE 30 bus test system

Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Solving practical economic load dispatch problem using crow search algorithm

The practical economic load dispatch problem is a non-convex, non-smooth, and non-linear optimization problem due to including practical considerations such as valve-point loading effects and multiple fuel options. An optimization algorithm named crow search algorithm is proposed in this paper to solve the practical non-convex economic load dispatch problem. Three cases with different economic load dispatch configurations are studied. The simulation results and statistical analysis show the efficiency of the proposed crow search algorithm. Also, the simulation results are compared to the other reported algorithms. The comparison of results confirm the high-quality solutions and the effectiveness of the proposed method for solving the non-convex practical economic load dispatch problem

ENHANCEMENT OF QoS IN MULTIMEDIA TRANSMISSION THROUGH OPTIMAL DELAY BASED FRAGMENTATION

ABSTRACT With the growth of different networking technology and multimedia technology the real time delivery of multimedia content becomes an imperative field. Most of the applications such as video conferencing need multimedia transmission techniques that send multimedia data from one end to another with enhanced efficiency in quality and minimized delay. Conventional packet fragmentation schemes shed a packet if all its fragments are not received correctly. But video data is loss tolerant and delay-sensitive. In this paper we propose a new family of delay based fragmentation algorithm which reduces the packet loss and delay thereby attain Quality of service in Multimedia applications

Scientific research trends about metaheuristics in process optimization and case study using the desirability function

This study aimed to identify the research gaps in Metaheuristics, taking into account the publications entered in a database in 2015 and to present a case study of a company in the Sul Fluminense region using the Desirability function. To achieve this goal, applied research of exploratory nature and qualitative approach was carried out, as well as another of quantitative nature. As method and technical procedures were the bibliographical research, some literature review, and an adopted case study respectively. As a contribution of this research, the holistic view of opportunities to carry out new investigations on the theme in question is pointed out. It is noteworthy that the identified study gaps after the research were prioritized and discriminated, highlighting the importance of the viability of metaheuristic algorithms, as well as their benefits for process optimization

A multilevel image thresholding based on Hybrid Salp Swarm algorithm and Fuzzy Entropy

The image segmentation techniques based on multi-level threshold value received lot of attention in recent years. It is because they can be used as a pre-processing step in complex image processing applications. The main problem in identifying the suitable threshold values occurs when classical image segmentation methods are employed. The swarm intelligence (SI) technique is used to improve multi-level threshold image (MTI) segmentation performance. SI technique simulates the social behaviors of swarm ecosystem, such as the behavior exhibited by different birds, animals etc. Based on SI techniques, we developed an alternative MTI segmentation method by using a modified version of the salp swarm algorithm (SSA). The modified algorithm improves the performance of various operators of the moth-flame optimization (MFO) algorithm to address the limitations of traditional SSA algorithm. This results in improved performance of SSA algorithm. In addition, the fuzzy entropy is used as objective function to determine the quality of the solutions. To evaluate the performance of the proposed methodology, we evaluated our techniques on CEC2005 benchmark and Berkeley dataset. Our evaluation results demonstrate that SSAMFO outperforms traditional SSA and MFO algorithms, in terms of PSNR, SSIM and fitness value