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Genetic algorithm A genetic algorithm is a search method used for obtaining an optimal solution which is based on evolutionary techniques that are similar to processes in evolutionary biology, including inheritance, learning, selection and mutation. The process starts with a population of candidate solutions called individuals, and progresses through generations, with the fitness of each individual being evaluated. Fitness is defined based on the objective function. Then multiple individuals are selected from the current generation based on fitness and modified to form a new population. This new population is used in the next iteration and the algorithm progresses toward the desired optimal point [94, 95]. Artificial neural network Artificial neural networks are interconnected groups of processing elements, called artificial neurons, similar to those in the central nervous system of the body. The approach is thus analogous to some elements of neuroscience. The characteristics of the processing elements and their interconnections determine the processing of information and the modeling of simple and complex processes. Functions are performed in parallel and the networks have both non- adaptive and adaptive elements, which change with the inputs and outputs and the problem. The ANN approach leads to nonlinear, distributed, parallel, local processing and adaptive representations of systems [93]. Fuzzy logic Fuzzy logic allows us to deal with inherently imprecise concepts, such as cold, warm, very, and slight, and is useful in a wide variety of thermal systems where approximate, rather than precise, reasoning is needed. Fuzzy logic can be used for the control of systems and in problems where a sharp cut off between two conditions does not exist. Multi-objective optimization Optimal conditions are generally strongly dependent on the chosen objective function. However, several aspects of performance are often important in most practical applications. In thermal and energy systems design, efficiency (energy and/or exergy), production rate, output, quality, and heat transfer rate are common quantities that are to be maximized, while cost, input, environmental impact and pressure are quantities to be minimized. Any of these can be chosen as the objective function for a problem, but it is usually more meaningful and useful to consider 66PDF Image | Energy Systems for Multigeneration Purposes
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