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Gain insight into the world of nature-inspired optimization techniques and algorithms. This book will prepare you to apply different nature-inspired optimization techniques to solve problems using Java. You'll start with an introduction to the hidden algorithms that nature uses and find the approximate solutions to optimization problems. You'll then see how living creatures such as fish and birds are able to perform computation to solve specific optimization tasks. This book also covers various nature-inspired algorithms by reviewing code examples for each one followed by crisp and clear explanations of the algorithm using Java code. You'll examine the use of each algorithm in specific industry scenarios such as fleet scheduling in supply chain management, and shop floor management in industrial and manufacturing applications. Nature-Inspired Optimization Algorithms with Java is your pathway to understanding a variety of optimization problems that exist in various industries and domains and it will develop an ability to apply nature-inspired algorithms to find approximate solutions to them. You will: Study optimization and its problems Examine nature-inspired algorithms such as Particle Swarm, Gray wolf, etc. See how nature-inspired algorithms are being used to solve optimization problems Use Java for solving the different nature-inspired algorithms with real-world examples.

Programming --- Computer architecture. Operating systems --- Open Source --- Java (informatica) --- Mathematical optimization. --- Nature-inspired algorithms. --- Java (Computer program language)

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Mathematical optimization. --- Nature-inspired algorithms. --- Algorithms --- Natural computation --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis

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This book presents a wide range of optimization methods and their applications to various electrical power system problems such as economical load dispatch, demand supply management in microgrids, levelized energy pricing, load frequency control and congestion management, and reactive power management in radial distribution systems. Problems related to electrical power systems are often highly complex due to the massive dimensions, nonlinearity, non-convexity and discontinuity associated with objective functions. These systems also have a large number of equality and inequality constraints, which give rise to optimization problems that are difficult to solve using classical numerical methods. In this regard, nature inspired optimization algorithms offer an effective alternative, due to their ease of use, population-based parallel search mechanism, non-dependence on the nature of the problem, and ability to accommodate non-differentiable, non-convex problems. The analytical model of nature inspired techniques mimics the natural behaviors and intelligence of life forms. These techniques are mainly based on evolution, swarm intelligence, ecology, human intelligence and physical science. .

Electrical engineering. --- Energy systems. --- Mathematics. --- Mathematical optimization. --- Electrical Engineering. --- Energy Systems. --- Mathematics, general. --- Optimization. --- Nature-inspired algorithms. --- Algorithms --- Natural computation --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Math --- Science --- Electric engineering --- Engineering

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The vast majority of real-world problems can be expressed as an optimisation task by formulating an objective function, also known as cost or fitness function. The most logical methods to optimise such a function when (1) an analytical expression is not available, (2) mathematical hypotheses do not hold, and (3) the dimensionality of the problem or stringent real-time requirements make it infeasible to find an exact solution mathematically are from the field of Evolutionary Computation (EC) and Swarm Intelligence (SI). The latter are broad and still growing subjects in Computer Science in the study of metaheuristic approaches, i.e., those approaches which do not make any assumptions about the problem function, inspired from natural phenomena such as, in the first place, the evolution process and the collaborative behaviours of groups of animals and communities, respectively. This book contains recent advances in the EC and SI fields, covering most themes currently receiving a great deal of attention such as benchmarking and tunning of optimisation algorithms, their algorithm design process, and their application to solve challenging real-world problems to face large-scale domains.

dynamic stream clustering --- online clustering --- metaheuristics --- optimisation --- population based algorithms --- density based clustering --- k-means centroid --- concept drift --- concept evolution --- imbalanced data --- screening criteria --- DE-MPFSC algorithm --- Markov process --- entanglement degree --- data integration --- PSO --- robot --- manipulator --- analysis --- kinematic parameters --- identification --- approximate matching --- context-triggered piecewise hashing --- edit distance --- fuzzy hashing --- LZJD --- multi-thread programming --- sdhash --- signatures --- similarity detection --- ssdeep --- maximum k-coverage --- redundant representation --- normalization --- genetic algorithm --- hybrid algorithms --- memetic algorithms --- particle swarm --- multi-objective deterministic optimization, derivative-free --- global/local optimization --- simulation-based design optimization --- wireless sensor networks --- routing --- Swarm Intelligence --- Particle Swarm Optimization --- Social Network Optimization --- compact optimization --- discrete optimization --- large-scale optimization --- one billion variables --- evolutionary algorithms --- estimation distribution algorithms --- algorithmic design --- metaheuristic optimisation --- evolutionary computation --- swarm intelligence --- memetic computing --- parameter tuning --- fitness trend --- Wilcoxon rank-sum --- Holm–Bonferroni --- benchmark suite --- data sampling --- feature selection --- instance weighting --- nature-inspired algorithms --- meta-heuristic algorithms

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The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods.

Advanced Metering Infrastructure (AMI) --- Distributed Energy Resources (DER) --- Distribution Management System (DMS) --- Graph Reduction in Parallel (GRIP) --- Intelligent Electronic Device (IED) --- Intelligent Platform Management Interface (IPMI) --- Service Oriented Architecture (SOA) --- Ultra Large-Scale System (ULSS) --- photovoltaic systems --- MPPT technique --- partial shading --- global MPP (GMPP) --- nature-inspired algorithms --- congestion management --- power flow --- generator rescheduling --- Flower Pollination Algorithm (FPA) --- Pumped Hydro Storage Unit (PHSU) --- ancillary services --- grid --- inverter --- PV --- reactive power --- solar --- Quasi-Z source inverter (QZSI) --- Y source inverter (YSI) --- energy storage system (ESS) --- hybrid renewable energy sources (HRES) --- demand --- load --- RBFNOEHO technique --- common mode current --- common mode voltage --- modulation techniques --- electromagnetic interference --- mitigation --- grid connected inverters --- rotor angle --- small signal stability --- householder algorithm --- power systems --- electric vehicles --- charging station --- transformer --- Energy PLAN --- renewable energy --- maximum demand (MD) --- solar PV --- battery energy storage system (BESS) --- net energy metering (NEM) --- maximum demand reduction (MDRed) model --- power quality --- voltage variations --- PV system --- aggregation times --- correlation analysis --- harmonic analysis --- wavelet transform --- wavelet packet --- measurement techniques --- cloud services --- trust management --- secure computing --- smart meter --- LBSS --- user-aware power regulatory model