VHDL-AMS based genetic optimisation of fuzzy logic controllers

Purpose – This paper presents a VHDL-AMS based genetic optimisation methodology for fuzzy logic controllers (FLCs) used in complex automotive systems and modelled in mixed physical domains. A case study applying this novel method to an active suspension system has been investigated to obtain a new type of fuzzy logic membership function with irregular shapes optimised for best performance. Design/methodology/approach – The geometrical shapes of the fuzzy logic membership functions are irregular and optimised using a genetic algorithm (GA). In this optimisation technique, VHDL-AMS is used not only for the modelling and simulation of the FLC and its underlying active suspension system but also for the implementation of a parallel GA directly in the system testbench. Findings – Simulation results show that the proposed FLC has superior performance in all test cases to that of existing FLCs that use regular-shape, triangular or trapezoidal membership functions. Research limitations – The test of the FLC has only been done in the simulation stage, no physical prototype has been made. Originality/value – This paper proposes a novel way of improving the FLC’s performance and a new application area for VHDL-AMS

การจัดสมดุลที่มีหลายวัตถุประสงค์บนสายการประกอบแบบขนานผลิตภัณฑ์ผสมด้วยการหาค่าที่เหมาะสมที่สุดแบบการกระจายตัวของสิ่งมีชีวิตตามภูมิศาสตร์

บทคัดย่อการหาค่าที่เหมาะสมที่สุดแบบการกระจายตัวของสิ่งมีชีวิตตามภูมิศาสตร์ (Biogeography-based Optimization:BBO) เป็นเมตาฮิวริสติกเชิงวิวัฒนาการที่ได้รับแนวคิดมาจากพฤติกรรมการอพยพของสิ่งมีชีวิตบนเกาะต่างๆบทความนี้นำเสนออัลกอริทึม BBO เพื่อใช้สำหรับแก้ปัญหาการจัดสมดุลที่มีหลายวัตถุประสงค์บนสายการประกอบแบบขนานผลิตภัณฑ์ผสม โดยมีวัตถุประสงค์จำนวนทั้งสิ้น 4 วัตถุประสงค์ที่จะถูกทำให้เหมาะสมที่สุดไปพร้อมๆ กัน ได้แก่จำนวนสถานีงานน้อยที่สุด จำนวนสถานีน้อยที่สุด ความสมดุลของภาระงานระหว่างสถานีงานสูงที่สุด และความสัมพันธ์ของงานสูงที่สุด ผลจากการทดลองแสดงให้เห็นอย่างชัดเจนว่า BBO มีสมรรถนะในการแก้ปัญหาที่สูงกว่าอัลกอริทึมเชิงพันธุกรรมแบบการจัดลำดับที่ไม่ถูกครอบงำ II (Non-dominated Sorting Genetic Algorithm II: NSGA-II) ซึ่งเป็นอีกอัลกอริทึมหนึ่งที่เป็นที่นิยม ทั้งในด้านการลู่เข้าสู่กลุ่มคำตอบที่เหมาะสมที่สุดแบบพาเรโต การกระจายตัวของกลุ่มคำตอบ อัตราส่วนของคำตอบที่ไม่ถูกครอบงำและเวลาที่ใช้ในการคำนวณหาคำตอบคำสำคัญ: สายการประกอบแบบขนานผลิตภัณฑ์ผสม การจัดสมดุลหลายวัตถุประสงค์การหาค่าที่เหมาะสมที่สุด แบบการกระจายตัวของสิ่งมีชีวิตตามภูมิศาสตร์AbstractBiogeography-based Optimization (BBO) is an evolutionary metaheuristic inspired by migratory behavior of species among islands. This article presents a BBO algorithm for solving multi-objective mixed-model parallel assembly line balancing problem where four objectives are optimized simultaneously; i.e. to minimize the number of workstations, to minimize the number of stations, a maximization of workload balancing between workstations, and placing an emphasis on maximizing work relatedness. The results from experiments clearly show that BBO promises better performance than does Non-dominated Sorting Genetic Algorithm II (NSGA-II), which indicates another well-known algorithm, in terms of convergence to the Pareto-optimal set, spread of solutions, ratio of non-dominated solutions, and computation time to solution.Keywords: Mixed-model Parallel Assembly Lines, Multi-objective Line Balancing, Biogeography-based Optimizatio

Comparative study of different approaches to solve batch process scheduling and optimisation problems

Effective approaches are important to batch process scheduling problems, especially those with complex constraints. However, most research focus on improving optimisation techniques, and those concentrate on comparing their difference are inadequate. This study develops an optimisation model of batch process scheduling problems with complex constraints and investigates the performance of different optimisation techniques, such as Genetic Algorithm (GA) and Constraint Programming (CP). It finds that CP has a better capacity to handle batch process problems with complex constraints but it costs longer time

Algorithms for Large-scale Whole Genome Association Analysis

In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for millions of polymorphisms. When handling these datasets, which exceed the main memory of contemporary computers, one faces two distinct challenges: 1) Millions of polymorphisms come at the cost of hundreds of Gigabytes of genotype data, which can only be kept in secondary storage; 2) the relatedness of the test population is represented by a covariance matrix, which, for large populations, can only fit in the combined main memory of a distributed architecture. In this paper, we present solutions for both challenges: The genotype data is streamed from and to secondary storage using a double buffering technique, while the covariance matrix is kept across the main memory of a distributed memory system. We show that these methods sustain high-performance and allow the analysis of enormous datase

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