Determination of Optimum Location of DG for Loss Reduction with Ant Colony Algorithm| Chapter 7 | Techniques and Innovation in Engineering Research Vol. 2

 Deregulation of the power system and variations in the power demand on a network result in an issue with a critical load with a low voltage profile. The issue could be resolved by injecting power at a place near the load point by placing a Distribution Generation (DG) on the networks. In order to get the most out of DG, the chosen substation must be positioned properly. A bad distribution of DG on the network could increase power loss and risk system functionality. This study used a genuine rural Malaysian network to introduce the ACO-algorithm for DG locating. The Brute Force approach was used to validate the ACO results. By analysing the power loss and changes in the voltage profiles after and before locating the DG, the approaches will establish its ideal position.

Author(s) Details:

S. Omar,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Muhammad Nasrullah Abdul Manan,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Muhammad Naqib Mat Siam,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Ahmad Asri Abd Samat,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Kamarul Azhar Daud,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Please see the link here: https://stm.bookpi.org/TAIER-V2/article/view/8275

Reinforcement Learning and Heuristic Algorithms for Efficient Routing Protocols in Mobile Ad-Hoc Networks: An Advanced Approach | Chapter 5 | Recent Advances in Mathematical Research and Computer Science Vol. 3

 Nodes in mobile ad-hoc networks can freely roam and communicate with each other across a wireless network in their frequency range. The routes are not stable due to the dynamic topology. As a result, one of the primary challenges is sending data packets between nodes. The methods that are consistent with the network changes caused by node migrations are quite important. Route brevity and route stability should both be considered while minimising data packet transmission time between nodes. Our approach to Artificial Intelligence was widely regarded as a novel development in the world of routing protocols more than a decade ago [1,2]. Reinforcement learning was utilised to pick the resilient routing method by determining the optimal choice among neighbour nodes at any given time to transport data packets from source to destination. It uses reinforcement learning to predict the behaviour pattern of the nodes in respect to the target node. The suggested method uses a Q-learning algorithm to assess the value of actions, which is more homogeneous [3].

Author(S) Details

D. Srinivas Reddy
Department of Computer Science, Vaageswari College of Engineering – Karimnagar, Telangana, India.

V. Bapuji
Department of Computer Science, Vaageswari College of Engineering – Karimnagar, Telangana, India.

A. Govardhan
Department of Computer Science & Engineering, JNTU-Hyderabad, Telangana, India.

View Book:- https://stm.bookpi.org/RAMRCS-V3/article/view/4466

Application of Ant Colony Optimization: An Approach towards Travelling Salesman Problem Resolution | Chapter 1 | Current Approaches in Science and Technology Research Vol. 13

 In today's society, everyone uses a vehicle for transportation, which results in a lot of air pollution, traffic, and time and fuel waste. It also causes driver unhappiness and costs billions of dollars in fuel use every year throughout the world. The dynamic and unpredictable nature of the organisation geography of vehicular situations, particularly in metropolitan zones, makes finding an acceptable solution for car clog a difficult task. The study's goal is to determine the quickest path to reduce the time spent travelling. There are several factors to consider when optimising an ant colony.The problem of the travelling salesman has been solved using a variety of ways. The purpose of this literature review is to learn how ant colony optimization methods are utilised to solve the travelling salesman issue and increase performance according to the requirements. The behaviour of ants has been seen to aid in the optimization of the ant colony algorithm. Other ants follow the pheromone trail left by the ant while walking, and ants can identify the shortest way of travel by following the pheromone trail. Similarly, the ACO is employed in TSP, where the algorithm assists in only visiting each city once with the least amount of distance and time.

Author (S) Details

Dr. Priyanka P. Shinde
Government College of Engineering Karad, Karad, Maharashtra, India.


Varsha P. Desai
Department of Studies, V.P. Institute of Management Studies & Research, Sangli, Maharashtra, India.


Dr.Kavita S. Oza
Computer Science Department, Shivaji University, Kolhapur, Maharashtra, India.


View Book :https://stm.bookpi.org/CASTR-V13/article/view/2798