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3-7 Optimization Algorithm 33 Figure 3-14: Pictorial representation of iterated local search (adapted from Kochenberger et. al. [56]) Starting with a local minimum entropy s∗, a perturbation is applied leading to an entropy s′. A local search is then applied to find the local minimum which is represented by s∗′. The values of s∗ and s∗′ are then compared to find the new minimum value of entropy. This procedure is carried out iteratively to minimize the entropy of the system and generate the required sample points which are then used as inputs to the optimization algorithm described in the following section. 3-7 Optimization Algorithm Table 3-8: Control parameters for the SOGA Tuning Parameter Iterations Population Size Crossover probability Crossover kind Mutation probability Mutation kind Seed Value 250 20 0.5 Full weight 0.1 Uniform 1000 An evolutionary algorithm, called Genetic Algorithm (GA), has been used which is ef- fective regardless of the shape of the objective function and the constraints. The algorithm combines the use of random numbers and information from previous iterations to evaluate and improve a population of points (a group of potential solutions) rather than a single point at a time. Based on the theory of the natural selection in the biological genetic progress which was developed by Charles Darwin, the GA features a fast sorting procedure and an elitism preservation mechanism which preserves the most optimum fitness during each iteration [57]. Master of Science Thesis Akshay Hattiangadi EntropyPDF Image | Working Fluid Design for Organic Rankine Cycle
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