Swarm based Optimization Algorithms for Task Allocation in Multi Robot Systems: A Comprehensive Review

Multi-robot systems (MRS) have gained significant attention due to their potential applications in various domains such as search and rescue, surveillance, and exploration. An essential aspect of MRS is task allocation, which involves distributing tasks among robots efficiently to achieve collective objectives. Swarm-based optimization algorithms have emerged as effective approaches for task allocation in MRS, leveraging principles inspired by natural swarms to coordinate the actions of multiple robots. This paper provides a comprehensive review of swarm-based optimization algorithms for task allocation in MRS, highlighting their principles, advantages, challenges, and applications. The discussion encompasses key algorithmic approaches, including ant colony optimization, particle swarm optimization, and artificial bee colony optimization, along with recent advancements and future research directions in this field

Application of Neuro-Fuzzy system to solve Traveling Salesman Problem

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) in solving the traveling salesman problem. Takagi-Sugeno-Kang neuro-fuzzy architecture model is used for this purpose. TSP, although, simple to describ

Implementing Multiple Security in the Cloud Environment

ABSTRACT: Cloud computing is continuously evolving and considered next generation architecture for computing. Typically, cloud computing is a combination of computing resources accessible via internet. Historically, the clients or the organizations store data in data centers with firewall and other security techniques to protect data against intruders. However, in cloud computing, since the data is stored anywhere across the globe, the client organizations have less control over the stored data. To build the trust for the growth of cloud computing, the cloud providers must protect the user data from unauthorized access and disclosure. Here in this work hybrid approach of encryption techniques and the storage of data are considered in the cloud system. The main advantage of the hybrid scheme is to provide more security in the cloud

Nutraceutical Potential of Seed and Grain Proteins in Health Promotion

In recent years, seed and grain proteins with nutritional bioactivity have been studied for disease prevention and treatments. Seed and grains are key components of a healthy and balanced diet which support the protective role of bioactive proteins with nutraceutical activities. Proteins obtained from seeds can be a good source of amino acids and nutraceutical peptides that can be used for biotic functions to improve health and disease prevention. Hence, the increased consumption of seeds and grains promotes a healthy generation in future and a significant reduction in diseases. To increase the human health awareness, we must have to enlighten the importance of easily available seeds and grains in our food

W-GUN: Whale Optimization for Energy and Delay centric Green Underwater Networks

Underwater Sensor Networks (UWSNs) has witnessed significant R&D attention in both academia and industries due to its growing application domain such as border security, freight via sea or river, natural petroleum production, etc. Considering the deep underwater oriented access constraints, energy centric communication for lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network without giving much attention to the realistic impact of underwater network environments resulting in degraded performance. Towards this end, this paper presents an adapted whale optimization algorithm-based energy and delay centric green UWSNs framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale centric optimization in relay node selection. Firstly, an underwater relay- node optimization model is mathematically derived focusing on whale and wolf optimization algorithms for incorporating realistic underwater characteristics. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm. Thirdly, a complete work-flow of the W-GUN framework is presented with the optimization flowchart. The comparative performance evaluation attests the benefits of the proposed framework as compared to the state-of-the-art techniques considering various metrics related to underwater network environments