Abstract
The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.
Highlights
The traveling salesman problem (TSP) is a typical non-deterministic polynomial (NP)hard problem with the goal of designing the shortest route for a traveler to visit each city without repetition, followed by returning to the starting city
The contributions of this work consist of three aspects: (1) The optimization strategy of multi-domain inversion is added after the crossover and mutation operations, with more offsprings to be selected in order to increase the probability of generating excellent individuals and avoid the population premature; (2) the MDIGA performs better in both reducing the optimal path length and enhancing the robustness than the conventional genetic algorithm (CGA); and (3) path-planning for a unmanned surface vehicles (USVs) is conducted using the MDIGA, which generates feasible routes with satisfactory length
The median values of the CGA and the double-domain inversion-based genetic algorithm (DDIGA) are smaller than their average values; this means the two algorithms are easier for producing larger data than the others in one hundred repeated simulations
Summary
The traveling salesman problem (TSP) is a typical non-deterministic polynomial (NP)hard problem with the goal of designing the shortest route for a traveler to visit each city without repetition, followed by returning to the starting city. The multi-domain inversion-based algorithm (MDIGA) is supposed to further enhance the local search capability since the offsprings are significantly increased, and only the inversed chromosome with the best fitness survives and is transferred to the new generation. The contributions of this work consist of three aspects: (1) The optimization strategy of multi-domain inversion is added after the crossover and mutation operations, with more offsprings to be selected in order to increase the probability of generating excellent individuals and avoid the population premature; (2) the MDIGA performs better in both reducing the optimal path length and enhancing the robustness than the CGA; and (3) path-planning for a USV is conducted using the MDIGA, which generates feasible routes with satisfactory length.
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