title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° ÑÐ¾Ð²Ð¼ÐµÑÑÐ½Ð¾Ð³Ð¾ Ñ Ð¿Ð¾Ð»ÐµÑÐ°Ð¼Ð¸ ÐÐµÐ²Ð¸ Ð´Ð»Ñ Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð·Ð°ÑÑÐ´ÐºÐ¸ ÑÐ»ÐµÐºÑÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: The study of a hybrid method of joint Levi flights for planning charging of electric vehicles
Creators: name: ÐÑÑÑÑÐµÐ², ÐÐ¸ÑÐ¸Ð»Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸
ÑÐ²Ð¾Ð»ÑÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐ²ÐµÑÐ»ÑÑÐºÐ¾Ð²
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐ¾Ñ ÑÐ°ÑÑÐ¸Ñ
Ð¿Ð¾Ð»ÐµÑÑ Ð»ÐµÐ²Ð¸
optimization
optimization methods
evolutionary algorithms
firefly algorithm
particle swarm optimization
levy flights

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description: This final qualifying work is devoted to the study of a new hybrid algorithm for planning the charging of electric vehicles. The algorithm develops an optimization framework that combines two algorithms, the Firefly algorithm (FFA) and particle swarm optimization (PSO), with Levy flights. This hybrid approach was developed and implemented by integrating three components: various exploration and exploitation capabilities of PSO and FFA; cooperation between them in the form of common global optima; and the use of a Levy flight search strategy to bypass local minima. For practical use, variables such as fast and slow charging options at the station were taken into account in the PZEM task. Since mixed strategies can give better results than a single optimization algorithm, three mixed strategies were investigated in this paper, including PSO, FFA, and Levy flights. These hybrid options have been evaluated to find the most effective hybrid option. This study presents the real application of this structure on two transport networks: Sioux Falls, South Dakota, USA; Guangzhou, China Tasks that were solved during the study: - study of the basic concepts of the algorithm, planning the charging of electric vehicles; - description of the algorithm implementation; - testing the algorithm; - analysis of test results. - comparison of the obtained data with other algorithms To assess the algorithms performance, it was tested on two transport networks, the Sioux Falls network is a classic reference urban network that is widely used in transport research, and Guangzhou is a major transport hub for electric vehicles in China, and a real set of data on electric vehicle chargers is available for transport research. Based on the test results obtained, conclusions were drawn about the effectiveness of the algorithm and an analysis of the control parameters was carried out using various testing scenarios.