Guided Multi-objective genetic algorithm
2016-08-23
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Three methods were proposed to guide the convergence of the genetic algorithm towards desired solutions: biasing the generation of the initial population in order to favour the exploration of a certain region of the search space, attributing different weights to the objectives and comparing the solutions according to the weighted sum of the objectives (WPMOGA), and including minimum and maximum trades-off in the comparison of solutions (G-MOGA). The three methods were tested on the network expansion problem in order to find solutions that imply adding few new transmission lines. The weighted sum method appeared to be inefficient, while the two other succeeded in guiding the random population towards solutions with few added lines. Biasing the initial population was the simplest to implement and furnished the best results, but the ease of using this method depend on the problem considered, and it might not be implementable in most multi-objectives problems. Contrariwise, the G-MOGA is u
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