Multiroute planning for aircraft using genetic algorithms

Abstract

SUMMARYThis paper proposes a scheme for generating multiple flight routes using genetic algorithms (GAs). In obtaining multiple routes or multiroutes by GAs simultaneously, there is a problem of collisions or near-misses occurring due to interference between the routes (closeness or intersecting of the routes) when similar routes are generated. The proposed scheme realizes a scheme for searching multiple flight routes without interference by incorporating a method for correcting their fitness on the basis of the neighborhood density for maintaining the variety and the degree of interference or the interference degree for obtaining routes that do not come close to or intersect one another. In addition, this paper proposes a priority candidate preserving strategy that preserves superior multiple individuals for each generation in order to improve the search performance. Computer simulation results confirm that the scheme proposed generates multiple flight routes without interference, indicating its efficacy.

A General-Purpose Path Generation Method Using Genetic Algorithms

“…Moreover, genetic algorithms generate multiple possible solutions in a search process, though the exhaustive search methods search for the optimum only. Hence, in the future, the proposed method will be able to obtain a set of multiple paths with small costs in a single iteration search process similarly to [4] and [8].…”

“…These paths are then represented by storing the distance h i from P i to X i in the ith locus in a one-dimensional array of loci in However, because the goal of the method in Ref. [8] was to generate flight paths, it is unable to represent paths that spiral in to reach the starting or ending point similar to that shown in Fig. 2.…”

“…2 using the genotype from Ref. [8]. The gene length would then vary among chromosomes, and we expect many lethal chromosomes created by performing crossover, causing a sudden drop in the diversity of the population of chromosomes after crossover.…”

“…Although Ref. [8] offers one example of path generation using a genetic algorithm, it is limited to generating flight paths for aircraft, and is therefore unable to create paths that deviate to a large extent from a straight line segment joining the specified starting and ending points. However, considering that the goal of this research is to apply path generation to general-purpose applications, it is necessary to be able to generate paths that, for example, spiral around the starting or ending point.…”

“…In the proposed method, paths that could not be represented by the method in Ref. [8] are made possible by designing a genotype with a fixed length and redundancy. Furthermore, deterioration of the search efficiency due to sudden reductions in the diversity of the population of chromosomes is prevented by using this genotype to suppress the generation of lethal chromosomes during crossover.…”