Standard NSGA-II algorithm based on real and binar
2016-08-23
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The characteristics of NSGA are as follows: & nbsp; L & nbsp; & nbsp; & nbsp; non dominated sorting: & nbsp; & nbsp; & nbsp; & nbsp; in the process of evolution, the current parent population is crossed and mutated to obtain a sub population, and the two populations are merged. In the objective space, individuals in the group are compared according to their objective function vectors according to the Pareto optimal relationship, and all individuals in the group are divided into multiple front layers which are controlled in turn. L & nbsp; & nbsp; & nbsp; fitness sharing: & nbsp; & nbsp; & nbsp; in the process of evolution, some strategies must be adopted to maintain the diversity of the population and prevent the population from eventually converging to a few individual solutions (i.e. premature convergence); & nbsp; & nbsp; & nbsp; NSGA method specifies the same fitness for individuals in the same layer, so as to ensure the distribution diversity of the population. For individuals close to a certain distance, the method of discount fitness is used to improve the coverage of frontier; Because individuals at the forefront have the greatest fitness, the more likely they are to be passed on to the next generation. The improvement of NSGA-II compared with NSGA method: l in NSGA-II, except for non dominated sorting, the operation rules are completely different from NSGA. In NSGA-II, the concept of archive is introduced. Compared with NSGA, Pareto frontier has become more reliable in its advance and expansion. Because the parent exploration population is generated from archive according to elimination selection, it exerts large selection pressure on individuals with high Pareto superiority (described in detail later). "& nbsp; & nbsp; & nbsp; this feature is manifested in the high forward ability of Pareto frontier. As an alternative method of fitness sharing in NSGA, "crowding distance" and“
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