New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Enhancing Medical Imaging with Swarm Intelligence Algorithms

Medical imaging serves as an indispensable tool for the diagnosis and continuous monitoring of a diverse array of health conditions. A recent and exciting development in this field is the integration of Swarm Intelligence (SI) algorithms, which draw inspiration from the collective behaviors observed in social insects. This collaborative effort between nature and technology is progressively transforming medical image analysis, elevating both its quality and efficiency. In this book chapter we have presented various SI optimization algorithms like ACO, BCO, FA, FSA and WOA in detail. By exploring these algorithms, we aim to provide an in-depth understanding of their respective benefits and limitations when applied to medical image analysis. This knowledge empowers practitioners to choose the most appropriate algorithm for specific tasks, ensuring optimal outcomes. Furthermore, we shed light on SI-Based Segmentation methodologies, elucidating the advantages and constraints associated with these approaches. The ability of SI algorithms to innovate in the realms of image segmentation, feature extraction, and classification is emphasized, with a focus on their potential to enhance diagnostic accuracy and elevate the quality of patient care. One of the most exciting prospects on the horizon is the amalgamation of SI with cutting-edge technologies like deep learning and big data analytics. This union has the potential to revolutionize medical imaging by providing solutions that are not only more accurate and efficient but also highly clinically relevant. As these developments continue to unfold, the synergy between SI and emerging technologies promises to reshape the medical imaging landscape, ultimately enhancing patient care and improving healthcare outcomes in unprecedented way

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC