Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus
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ORIGINAL PAPER
Analysis of asynchronous distributed multi‑master parallel genetic algorithm optimization on CAN bus Vahid Jamshidi1 · Vahab Nekoukar1 · Mohammad Hossein Refan1 Received: 29 September 2018 / Accepted: 10 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Industrial optimization problems are usually difficult to solve due to complexity and high number of constraints. Evolutionary algorithms are a conventional method to solve these problems. However, many industrial applications are real-time or we need to find a feasible optima solution in a limited time. Parallel genetic algorithm is a method to utilize properties of the genetic algorithm and parallel processing and implementation of a fast evolutionary algorithm. Controller Area Network (CAN) protocol is widely used in various industries such as automotive, medical, aerospace. In this paper, we implement a multiple-population coarse-grained parallel genetic algorithm on CAN bus to improve speed and performance of the conventional genetic algorithm which is asynchronous distributed multi-master. Evaluation criteria such as speed up, efficiency, serial fraction and reliability are calculated for the proposed parallel processing which is used for optimization problem of five benchmark functions. And finally, this structure is compared with the master–slave model. The proposed structure is created conditions for improving network reliability with very low cost of communication. Keywords CAN bus · Multi-master architecture · Parallel genetic algorithm · Busmaster simulator
1 Introduction Nowadays, optimality and optimization are not limited to theory applications. Many industrial engineers try to apply the optimization for design and implementation a better product such as optimal design of a car suspension or optimal design of a transit schedule (Deb 2004), optimization of an absorption heat transformer (Moralesa et al. 2015), modeling and optimization of microstructural properties in Al/SiC nanocomposite (Esmaeili and Dashtbayazi 2014), auto-tuning PID control system passive optical networks (Jimenez, et al. 2015) and voltage stability enhancement using VSC-OPF (Khatua and Yadav 2015). Due to growth of complexity in industrial applications and difficulty of finding an optimal or sub-optimal solution in a fissile search domain, * Vahab Nekoukar [email protected] Vahid Jamshidi [email protected] Mohammad Hossein Refan [email protected] 1
Electrical Engineering School, Shahid Rejaee Teacher Training University, Tehran, Iran
the engineers employ evolutionary computers as the optimization techniques for the complex problems for example evolutionary algorithms applications for image enhancement and segmentation ( Paulinas and Ušinskas 2007). Genetic algorithm (GA) is one of the most well-known and popular evolutionary algorithms (EAs) (Melanie 1998). The GA is a population-based search technique based on the fundamentals of genetics and natural selection. The method was proposed by Holland and it was fina
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