Altaf Q. H. Badar – författare

This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems.The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and includes software’s like MATLAB and Python for solving optimization problems. It covers important optimization algorithms including single objective optimization, multi objective optimization, Heuristic optimization techniques, shuffled frog leaping algorithm, bacteria foraging algorithm and firefly algorithm.Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering, this text: Provides step-by-step solution for each evolutionary optimization algorithm. Provides flowcharts and graphics for better understanding of optimization techniques. Discusses popular optimization techniques include particle swarm optimization and genetic algorithm. Presents every optimization technique along with the history and working equations. Includes latest software like Python and MATLAB.

This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems.The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and includes software’s like MATLAB and Python for solving optimization problems. It covers important optimization algorithms including single objective optimization, multi objective optimization, Heuristic optimization techniques, shuffled frog leaping algorithm, bacteria foraging algorithm and firefly algorithm.Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering, this text: Provides step-by-step solution for each evolutionary optimization algorithm. Provides flowcharts and graphics for better understanding of optimization techniques. Discusses popular optimization techniques include particle swarm optimization and genetic algorithm. Presents every optimization technique along with the history and working equations. Includes latest software like Python and MATLAB.

Battery-Integrated Residential Energy Systems introduces battery energy storage systems (BESS) of residential systems and offers insight into modeling, managing, and controlling them. Apart from survey of different BESS applications, it explains electrochemical simulation models of BESS. It includes performance parameters, economic analysis, sizing, energy management, control, charging and discharging patterns, coordination of the storage system with other devices in the home, and policy-related matters.Features:Covers different aspects of smart residential energy systems with a battery as a key elementDiscusses increasing energy efficiency in residential units by optimal control and management of BESSExplores information related to the policy regarding the use of BESSIncludes chapters on energy trading for implementation of optimal energy management of BESSFocuses on a variety of aspects of battery operation and controlThis book is aimed at senior undergraduate, graduate students, and researchers in Electrical Engineering, Battery Systems, Energy Engineering, and Sustainable and Renewable Technologies.