A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

Chen, Chen; Li, Meilian; Sui, Jisheng; Wei, Kangwen; Pei, Qingqi

doi:10.1002/atr.1426

Cited by 25 publications

(25 citation statements)

References 24 publications

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“…Constraints (8)- (9) guarantee that delivery and pickup centers, respectively, meet their customers' needs. Constraint (10) stipulates that pickup activities can either be performed by LCs or PCs, whereas constraint (11) allows only LCs and DCs to deliver products to customer units. Constraints (12)- (13) regulate the maximum travelling distance of pickup and delivery vehicles.…”

Section: Model Formulationmentioning

confidence: 99%

“…With the rising need of intelligent approaches for logistics network optimization, heuristic algorithms like the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) have been successfully applied in many domains [9,10]. Shimizu and Miura [11] proposed a multipopulation based discrete PSO algorithm in order to handle binary decision variables and optimize large-scale logistics networks.…”

Section: Introductionmentioning

confidence: 99%

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Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Wang

Yuan

Assogba

et al. 2018

Journal of Advanced Transportation

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In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN). The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units' reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS) model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.

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Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Wang

Yuan

Assogba

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

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“…To date, some popular evolutionary algorithms have been employed to develop metaheuristic fuzzy systems for engineering optimization. For example, GA (genetic algorithm)-fuzzy, PSO (particle swarm optimization)-fuzzy, and ACO (ant colony optimization)-fuzzy are powerful hybrid swarm intelligences for fuzzy structure optimization [7][8][9][10][11]. This hybrid computational intelligence is a modern technology for solving real-world engineering problems and complex multidimensional optimization problems [7].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles

et al. 2019

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This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.

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“…Furthermore, in the artificial intelligence (AI) discipline, fuzzy logic is often associated with the genetic algorithm (GA) [16] to solve many optimization tasks, from the steel structure analysis [17] to face recognition [18] and control of moving cars to avoid rear-end collisions [19]. Hence, in this research, a heuristic search continuous GA, which was inspired by the natural evolution, is modified to provide the higher optimal efficiency for faster searching and convergence.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Control Method for a Single Tilt Tricopter

et al. 2019

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This article proposes an adaptive fuzzy gain scheduling (FSG) design of the traditional proportional integral derivative (PID) control method by using fuzzy logic rules to schedule controlled gains at different phases. Owing to minimization of the tracking error of the controller design using three parameters and the integral of time weighted-squared error (ITSE) minima criterion of the controller design process, the fuzzy rules of the triangular membership functions are exploited online to verify the PID controller gains in different operated scheduling modes. For that reason, the controller designs can be used to tune the system models during the whole operation time period to enable efficient error tracking. The continuous genetic algorithm (GA) is considered an innovation because in it, the decode chromosome step is totally neglected. Owing to this improvement, it is superior to the standard GA because it requires less storage and enables naturally faster convergence. In this research, the controlled parameters were optimized using the continuous GA to enhance the efficiency of the proposed method. Thereafter, it was implemented to a single tilt Tricopter model to test whether the control performance is better when compared with the conventional PID control method. INDEX TERMS Fuzzy gain scheduling (FGS), proportional integral derivative (PID) control, continuous genetic algorithm (GA), integral of time weighted-squared error (ITSE), single tilt tricopter UAV.

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A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

Cited by 25 publications

References 24 publications

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles

Adaptive Fuzzy Control Method for a Single Tilt Tricopter

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