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The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization.

Process Flexibility --- Business Process Modeling --- Relational Reinforcement Learning --- Optimization --- Particle Swarm Optimization

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Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks --- n/a

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This book comprises five peer-reviewed articles covering original research articles on the modeling and simulation of electricity systems for transport and energy storage. The topics include: 1 - Optimal siting and sizing methodology to design an energy storage system (ESS) for railway lines; 2 - Technical–economic comparison between a 3 kV DC railway and the use of trains with on-board storage systems; 3 - How to improve electrical feeding substations, by changing transformer technology and by installing dedicated high-power-oriented storage systems; 4 - Algorithm applied to a vehicle-to-grid (V2G) technology. 5 - Thermal investigation and optimization of an air-cooled lithium-ion battery pack.

thermal management system --- optimal configuration --- air-cooling --- lithium-ion battery --- electric vehicles (EVs) --- photovoltaic (PV) systems --- vehicle-to-grid (V2G) --- smart grids (SGs) --- peak shaving --- amorphous transformer --- energy storage --- failure --- feeding substation --- tramway --- optimization --- energy storage system (ESS) --- siting --- sizing --- regenerative braking --- particle swarm optimization (PSO) algorithm --- net present value (NPV) --- railway network --- railway system --- lithium batteries --- supercapacitor --- Simulink --- catenary-free

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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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With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation.

electric vehicles --- PROSA --- PROMETHEE for Sustainability Assessment --- MCDA --- Multi-Criteria Decision Analysis --- stochastic analysis --- Monte Carlo --- uncertainty --- cargo bicycles --- loading hub --- facility location problem --- computer simulations --- Python programing --- electric vehicle charging --- vehicle-to-grid --- genetic algorithms --- particle swarm optimization --- demand-side management --- discrete choice theory --- revenue management --- road–railway accidents --- classification trees --- road safety --- transport means --- accidents victims --- condition monitoring --- vibroacoustic diagnostics --- gearbox --- power transmission systems --- neural networks --- deep learning --- n/a --- road-railway accidents