Secure Routing Environment with Enhancing QoS in Mobile Ad-Hoc Networks

A mobile adhoc network is infrastructure-free and self configured network connected without wire. As it is infrastructure-free and no centralized control, such type of network are suitable only for conditional inter communication link. So initially maintaining Quality of Service and security aware routing is a difficult task. The main purpose of QoS aware routing is to find an optimal secure route from source to destination which will satisfy two or more QoS constrain. In this paper, we propose a net based multicasting routing scheme to discovery all possible secure path using Secure closest spot trust certification protocol (SCSTC) and the optimal link path is derived from Dolphin Echolocation algorithm (DEA). The numerical result and performance analysis clearly describe that our provided proposal routing protocol generates better packet delivery ratio, decreases packet delay reduces overhead in secured environment

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes

Leo: Lagrange Elementary Optimization

Global optimization problems are frequently solved using the practical and efficient method of evolutionary sophistication. But as the original problem becomes more complex, so does its efficacy and expandability. Thus, the purpose of this research is to introduce the Lagrange Elementary Optimization (Leo) as an evolutionary method, which is self-adaptive inspired by the remarkable accuracy of vaccinations using the albumin quotient of human blood. They develop intelligent agents using their fitness function value after gene crossing. These genes direct the search agents during both exploration and exploitation. The main objective of the Leo algorithm is presented in this paper along with the inspiration and motivation for the concept. To demonstrate its precision, the proposed algorithm is validated against a variety of test functions, including 19 traditional benchmark functions and the CECC06 2019 test functions. The results of Leo for 19 classic benchmark test functions are evaluated against DA, PSO, and GA separately, and then two other recent algorithms such as FDO and LPB are also included in the evaluation. In addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA, FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate Leo's capacity to increase the starting population and move toward the global optimum. Different standard measurements are used to verify and prove the stability of Leo in both the exploration and exploitation phases. Moreover, Statistical analysis supports the findings results of the proposed research. Finally, novel applications in the real world are introduced to demonstrate the practicality of Leo.Comment: 28 page

The single row layout problem with clearances

The single row layout problem (SRLP) is a specially structured instance of the classical facility layout problem, especially used in flexible manufacturing systems. The SRLP consists of finding the most efficient arrangement of a given number of machines along one side of the material handling path with the purpose of minimising the total weighted sum of distances among all machine pairs. To reflect real manufacturing situations, a minimum space (so-called clearances) between machines may be required by observing technological constraints, safety considerations and regulations. This thesis intends to outline the different concepts of clearances used in literature and analyse their effects on modelling and solution approaches for the SRLP. In particular the special characteristics of sequence-dependent, asymmetric clearances are discussed and finally extended to large size clearances (machine-spanning clearances). For this, adjusted and novel model formulations and solution approaches are presented. Furthermore, a comprehensive survey of articles published in this research area since 2000 is provided which identify recent developments and emerging trends in SRLP

A NOVEL APPROACH TO ORBITAL DEBRIS MITIGATION

Since mankind launched the first satellite into orbit in 1957, we have been inadvertently, yet deliberately, creating an environment in space that may ultimately lead to the end of our space exploration. Space debris, more specifically, orbital debris is a growing problem that must be dealt with sooner, rather than later. Several ideas have been developed to address the complex problem of orbital debris mitigation. This research will investigate the possibility of removing orbital debris from the Low Earth Orbit (LEO) regime by using a metaheuristic algorithm to maximize collection of debris resulting from the February 2009 on-orbit collision of Iridium 33 and Cosmos 2251. This treatment will concentrate on the Iridium debris field for analysis. This research is necessary today, more than ever, as we embark on the launch of thousands of LEO spacecraft, which could result in the realization of the Kessler Syndrome, “The certain risk of failure on launch or during operations due to an on-orbit collision with debris” (Kessler & Cour-Palais, 1978)

Multi-Robot Learning with Bat Algorithm With Mutation (Bam)

The mobile robotics is an active area of research. Several methods are under study to increase and optimize the working capabilities of multi robotic systems. These multi robotic systems or robot swarms have vast applications in industry as a human assistant to carry goods and can-do variety of jobs. Multiple techniques like swarm optimization, cuckoo algorithm and other such algorithms are under study for multi robotic systems. In this research, a biological bat inspired algorithm is implemented to achieve the target. BAT algorithm is implemented to achieve the target. BAT algorithm uses echolocation technique like bats to generate bat population and random data is generated, the robot then traverses and the distance is calculated which is compared to the distance from the obstacle. The loop continues and robot keeps moving. For more than one robot, robots have statistic as well as dynamic obstacles. So, the traversal speed and efficiency of bad algorithm reduces slightly

HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

In swarm robotics, a group of robots coordinate with each other to solve a problem. Swarm systems can be heterogeneous or homogeneous. Heterogeneous swarms consist of multiple types of robots as opposed to Homogeneous swarms, which are made up of identical robots. There are cases where a Heterogeneous swarm system may consist of multiple Homogeneous swarm systems. Swarm robots can be used for a variety of applications. Swarm robots are majorly used in applications involving the exploration of unknown environments. Swarm systems are dynamic and intelligent. Swarm Intelligence is inspired by naturally occurring swarm systems such as Ant Colony, Bees Hive, or Bats. The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. In this paper, we study the advantages of fusing the Meta-Heuristic Bat Algorithm with Heuristic Optimization. We have implemented the Meta- Heuristic Bat Algorithm and tested it on a heterogeneous swarm. The same swarm has also been tested by segregating it into different homogeneous swarms by subjecting the heterogeneous swarm to a heuristic optimization