Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

https://www.mdpi.com/2079-9292/8/7/768/pdf

A Survey on Internet of Things and Cloud Computing for Healthcare

Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost every industry is trying to cope with this huge data. Big data phenomenon has begun to gain importance. However, it is not only very difficult to store big data and analyse them with traditional applications, but also it has challenging privacy and security problems. For this reason, this paper discusses the big data, its ecosystem, concerns on big data and presents comparative view of big data privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications an overall perspective of security and privacy issues in big data is suggested.

A survey on security and privacy issues in big data

Big Data and Hadoop-a Study in Security Perspective

We present an empirical comparison of two new meta-heuristics SSO and FP.Real test images were used to perform thresholding using Otsu's method and Kapur's entropy.Compared algorithms were SSO, FP, PSO, BAT.Comparisons were made according to the fitness values, PSNR and SSIM.SSO shows superior performance in convergence and in quality terms. In this paper, we investigate the ability of two new nature-inspired metaheuristics namely the flower pollination (FP) and the social spiders optimization (SSO) algorithms to solve the image segmentation problem via multilevel thresholding. The FP algorithm is inspired from the biological process of flower pollination. It relies on two basic mechanisms to generate new solutions. The first one is the global pollination modeled in terms of a Levy distribution while the second one is the local pollination that is based on random selection of local solutions. For its part, the SSO algorithm mimics different natural cooperative behaviors of a spider colony. It considers male and female search agents subject to different evolutionary operators. In the two proposed algorithms, candidate solutions are firstly generated using the image histogram. Then, they are evolved according to the dynamics of their corresponding operators. During the optimization process, solutions are evaluated using the between-class variance or Kapur's method. The performance of each of the two proposed approaches has been assessed using a variety of benchmark images and compared against two other nature inspired algorithms from the literature namely PSO and BAT algorithms. Results have been analyzed both qualitatively and quantitatively based on the fitness values of obtained best solutions and two popular performance measures namely PSNR and SSIM indices as well. Experimental results have shown that both SSO and FP algorithms outperform PSO and BAT algorithms while exhibiting equal performance for small numbers of thresholds. For large numbers of thresholds, it was observed that the performance of FP algorithm decreases as it is often trapped in local minima. In contrary, the SSO algorithmprovides a good balance between exploration and exploitation and has shown to be the most efficient and the most stable for all images even with the increase of the threshold number. These promising results suggest that the SSO algorithm can be effectively considered as an attractive alternative for the multilevel image thresholding problem.

Social spiders optimization and flower pollination algorithm for multilevel image thresholding

QoS enhancements for global replication management in peer to peer networks

A review on population seeding techniques for Permutation-coded Genetic Algorithm.Experiments are performed on large sized Traveling Salesman Problem instances.The experimentation analyses are carried out using ANOVA and DMRT statistical tools.Best performing population seeding techniques are identified w.r.t. assessment criteria. The genetic algorithm (GA) is a population based meta-heuristic global optimization technique for dealing with complex problems with very large search space. The population initialization is a crucial task in GA because it plays a vital role in the convergence speed, problem search space exploration and also the quality of the final optimal solution. Though the importance of deciding problem specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this paper, different population seeding techniques for permutation-coded genetic algorithm such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), selective initialization (SI) along with three newly proposed ordered distance vector based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large sized benchmark TSP instances obtained from standard TSPLIB. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

Performance analyses over population seeding techniques of the permutation-coded genetic algorithm

Internet of Things (IoT) is an interconnection among the objects, equipped with ubiquitous intelligence, used by human in day-today life. A large scale of advancement in wireless technology, sensing technology and communication technology takes the efficacy of the IoT to the next level. Many papers have studied the different concepts of IoT but a single-stop work on complete IoT technology study is still area of research. In this perspective, a comprehensive survey on IoT and its enabling technologies are discussed in the work. This paper also concentrates on the service layered architectures with its protocols and different perspectives of IoT. This work would stand as single-point complete study for the researchers and to derive new problem identifications in the domain.

P. Victer Paul

Papers

Big Data and Hadoop-a Study in Security Perspective

QoS enhancements for global replication management in peer to peer networks

Performance analyses over population seeding techniques of the permutation-coded genetic algorithm

The Internet of Things — A comprehensive survey

A Survey on Predictive Models of Learning Analytics