عنوان انگلیسی مقاله:
Success history applied to expert system for underwater glider path planning using differential evolution
ترجمه فارسی عنوان مقاله:
تاریخچه موفقیت برای برنامه ریزی مسیر گلایدر در زیر آب با استفاده از تکامل افتراقی برای سیستم خبره کاربردی
Sciencedirect - Elsevier - Expert Systems With Applications, 119 (2019) 155-170: doi:10:1016/j:eswa:2018:10:048
AlešZamuda a , ∗, JoséDaniel Hernández Sosa b
This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) includ- ing Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from lit- erature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is target- ing higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms ex- isting literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision- makers, when they use the improved UGPP expert system yielding better trajectories.
Keywords: Differential evolution | Linear population size reduction | Success-history based parameter adaptation | L-SHADE | Underwater glider path planning