In addition, automation provides the computational power needed to find optimal or near-optimal assembly plans, even for complex mechanical products. The simulation results show that by implementing this local search method in the form of a new genetic operator, the speed of convergence to the optimum solution is noticeably increased.Īutomated assembly planning reduces manufacturing manpower requirements and helps simplify product assembly planning, by clearly defining input data, and input data format, needed to complete an assembly plan. This local optimizer, which tries to improve the fitness of one chromosome in the population, effectively uses the information generated in calculating the fitness. In this research work, in order to improve the convergence of the GA, a new local optimizer for the UC problem based on Lamarck theory in the evolution, has been proposed.
Nevertheless, since the GA does not effectively use all the available information, usually the searching process does not have satisfactory convergence. The genetic algorithm (GA), as a powerful tool to achieve global optima, has been successfully used for the solution of this complex optimization problem. In mathematical terms, UC is a nonlinear optimization problem with a varied set of constraints. Unit commitment (UC) is an important optimization task in the daily operation planning of the utilities.
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