Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods

Retraction Retracted: Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods

A Comprehensive Analysis on Risk Prediction of Heart Disease using Machine Learning Models

Most of the deaths worldwide are caused by heart disease and the disease has become a major cause of morbidity for many people. In order to prevent such deaths, the mortality rate can be greatly reduced through regular monitoring and early detection of heart disease. Heart disease diagnosis has grown to be a challenging task in the field of clinically provided data analysis. Predicting heart disease is a highly demanding and challenging task with pure accuracy, but it is easy to figure out using advanced Machine Learning (ML) techniques. A Machine Learning approach has been shown to predict heart disease in this approach. By doing this, the disease can be predicted early and the mortality rate and severity can be reduced. The application of machine learning techniques is advancing significantly in the medical field. Interpreting these analyzes in this methodology, which has been shown to specifically aim to discover important features of heart disease by providing ML algorithms for predicting heart disease, has resulted in improved predictive accuracy. The model is trained using classification algorithms such as Decision Tree (DT), K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector Machine (SVM). The performance of these four algorithms is quantified in different aspects such as accuracy, precision, recall and specificity. SVM has been shown to provide the best performance in this approach for different algorithms although the accuracy varies in different cases

Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency

Optimization algorithms for adaptive filtering of interferences in corrupted signal

274-281Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designed using the adaptive filtering of the interferences in the corrupted signal using the Back Propagation Neural Network (BPNN) algorithm, Genetic Algorithm (GA), and Bee Colony (BC) algorithm. These optimization algorithms are used for initialization of weights, learning parameters, activation function and selection of network structure of the artificial neural network. The adaptive filtering system is designed using an adaptive learning ability of BPNN algorithm. This paper presents a comparison of evolutionary optimization algorithm such as hybrid GA-BPNN and BC-BPNN algorithm for the interference cancellation in corrupted signals

Solution to Optimization Problem Through Evolutionary Algorithm Using Weighting Function Method

The need for reduction in emission of harmful gases such as carbon dioxide, sulfur dioxide and nitrogen oxides from fossil fuel fired power plants has attracted our attention since it pollutes the atmosphere. So an attempt has been made to combine the concept of emission dispatch along with an economic dispatch problem with the help of the ABC algorithm using the weighting function method in power system operation. To apply this algorithm the original problem of optimization has been changed into the problem of identifying the best parameter that optimizes the objective function. This algorithm has shown its effectiveness in solving many real world problems with many constraints in different domains. The designed approach has been tested using Standard IEEE 30 bus system with incremental cost function along with emission coefficients valve opening and closing effects in the power plant. The result obtained shows that the designed approach was identified to be the best and most efficient in identifying the global minimum among the search space compared with other existing techniques

Optimization algorithms for adaptive filtering of interferences in corrupted signal

Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designed using the adaptive filtering of the interferences in the corrupted signal using the Back Propagation Neural Network (BPNN) algorithm, Genetic Algorithm (GA), and Bee Colony (BC) algorithm. These optimization algorithms are used for initialization of weights, learning parameters, activation function and selection of network structure of the artificial neural network. The adaptive filtering system is designed using an adaptive learning ability of BPNN algorithm. This paper presents a comparison of evolutionary optimization algorithm such as hybrid GA-BPNN and BC-BPNN algorithm for the interference cancellation in corrupted signals

Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network

Wireless Sensor Network (WSN) provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS) algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI) and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios

PSO Based Optimal Location and Sizing of SVC for Novel Multiobjective Voltage Stability Analysis during N – 2 Line Contingency

In this paper voltage stability is analysed based not only on the voltage deviations from the nominal values but also on the number of limit violating buses and severity of voltage limit violations. The expression of the actual state of the system as a numerical index like severity, aids the system operator in taking better security related decisions at control centres both during a period of contingency and also at a highly stressed operating condition. In contrary to conventional N – 1 contingency analysis, Northern Electric Reliability Council (NERC) recommends N – 2 line contingency analysis. The decision of the system operator to overcome the present contingency state of the system must blend harmoniously with the stability of the system. Hence the work presents a novel N – 2 contingency analysis based on the continuous severity function of the system. The study is performed on 4005 possible combinations of N – 2 contingency states for the practical Indian Utility 62 bus system. Static VAr Compensator is used to improve voltage profile during line contingencies. A multi- objective optimization with the objective of minimizing the voltage deviation and also the number of limit violating bus with optimal location and optimal sizing of SVC is achieved by Particle Swarm Optimization algorithm