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Unleashing the two-dimensional benefits of connected and automated vehicles via dedicated intersections in mixed traffic

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 160卷

作者： Zhang, Jiawei Chang, Cheng Li, Shen Ban, Xuegang (Jeff) Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Dept Civil Engn Beijing Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA

The management of mixed traffic systems is critical to realize the benefits of connected and automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into the onedimensional benefits of improving car-following performance and the two-dimensional benefits of efficiently addressing right-of-way conflicts. Researchers have proposed dedicated lanes that can exploit the one-dimensional benefits of CAVs, but the two-dimensional benefits remain unexplored in mixed traffic environments. To fully release the benefits of CAVs, we introduce an innovative approach for managing mixed traffic in road networks, known as the dedicated intersection strategy. Specifically, the dedicated intersection strategy refers to constructing twodimensional CAV-dedicated right-of-way within the road network, achieving separation between CAVs and human driven vehicles (HDVs) at the intersection level. However, the deployment problem of dedicated intersections is an NP-hard problem. Therefore, we propose a bi-level solving framework, in which the upper level determines the dedicated intersection deployment scheme and the lower level solves the corresponding traffic assignment problem. To quickly find adequate deployment schemes, we propose an artificial bee colony based intelligent algorithm. Our numerical experiments demonstrate that the proposed algorithm can quickly find nearoptimal dedicated intersection deployment schemes within a small number of iterations. Compared to regular intersections, the deployment of dedicated intersections in the road network can improve overall traffic system efficiency, particularly for CAVs. Finally, microscopic traffic simulation experiments further verify the superior performance of the proposed dedicated intersection strategy.

关键词： Dedicated intersection Mixed traffic artificial bee colony algorithm Connected and automated vehicles (CAVs)

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Damage localization in a cable-stayed bridge via bio-inspired metaheuristic tools

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STRUCTURAL CONTROL & HEALTH MONITORING 2017年第5期24卷 pn/a-N.PAG页

作者： Casciati, Sara Elia, Lorenzo Univ Catania Siracusa Dept Civil Engn & Architecture DICAr Piazza Federico di Svevia 1 I-96100 Siracusa Italy Univ Pavia Dept Civil Engn & Architecture DICAr Via Ferrata 3 I-27100 Pavia Italy

Structural health monitoring is conceived to detect abnormal behaviors in structural systems. A highly non-linear objective function built on the discrepancies between true and generated modal features can be minimized for this purpose. After a finite element discretization is built, the design variables are chosen, and the optimization problem solved. Two bio-inspired metaheuristic tools, namely the artificial bee colony and the firefly algorithm, are employed to proceed toward the global minima. Comparing both identified and analytical stiffness matrices, the damage localization is performed. These methods are tested on a cable-stayed bridge placed in northern Italy. The efficiency of these tools is compared. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： artificial bee colony algorithm damage localization cable-stayed bridge firefly algorithm metaheuristic algorithms optimization

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An NSABC algorithm for multi-aisle AS/RS scheduling optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107254-107254页

作者： Yan, Xiaohui Zhang, Zhicong Liu, Qunfeng Lv, Cixing Zhang, Liangwei Li, Shuai Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

A multi-objective scheduling optimization model for multi-aisle AS/RS is established in this study, considering the sequence constraint of inbound and outbound tasks with a multi-carrier S/R machine. The aisle switching time is redefined according to the most common layout, which is more realistic than in some of the literature. A non-dominated sorting artificial bee colony (NSABC) algorithm is proposed to solve the problem, which combines the multi-objective handling framework of NSGA-II and the neighborhood search of the ABC algorithm. The encoding structure, decoding method, and updating operators of the solutions are redesigned according to the features of the model. In experiments, operators and parameters of the NSABC algorithm are tested and fine-tuned. The best version is then tested on several groups of instances and compared with the EMBBO, NSGA-II, and MOEA/D algorithms. The results show that the proposed NSABC algorithm outperforms the comparison algorithms in most instances, which indicates that it is an effective approach for the AS/RS scheduling optimization problem.

关键词： Scheduling optimization Multi-objective optimization Automated storage and retrieval system Non-dominated sorting artificial bee colony algorithm

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Robust fault detection and isolation scheme using fuzzy wavelet network with a hybrid design algorithm

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SCIENTIA IRANICA 2017年第3期24卷 1467-1481页

作者： Shahriari-kahkeshi, M. Shahrekord Univ Fac Engn POB 115 Shahrekord Iran

This paper presents a new robust fault detection and isolation scheme using fuzzy wavelet network based on the bounded error approach. An efficient hybrid design algorithm, which consists of the orthogonal least square and the artificial bee colony algorithms, is proposed to design fuzzy wavelet network for modeling normal and faulty behaviors of the system. The proposed model provides an alternative description of the behavior of the system with high accuracy, but it suffers from model uncertainty because of model-reality mismatch in practical applications. To overcome this difficulty, the bounded error approach inspired from robust identification theory is applied to estimate the model uncertainty which defines a confidence interval of the model output and derives adaptive threshold for residual evaluation. Also, online fault isolation process is performed using fuzzy wavelet network models of the faulty system and analyzing the relation between a bank of residuals. Performance and efficiency of the proposed scheme is evaluated by simulating the nonlinear two-tank liquid level control system. Finally, some performance indexes are defined, and then the Monte-Carlo analysis is carried out to evaluate the reliability and robustness of the proposed scheme. (C) 2017 Sharif University of Technology. All rights reserved.

关键词： Robust fault detection and isolation Fuzzy wavelet network Adaptive threshold generation Bounded-error approach artificial bee colony algorithm

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A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期197卷

作者： Ramachandran, Murugan Mirjalili, Seyedali Ramalingam, Mohan Malli Gnanakkan, Christober Asir Rajan Charles Parvathysankar, Deiva Sundari Sundaram, Arunachalam KCG Coll Technol Dept Elect & Elect Engn Chennai 600097 Tamil Nadu India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld Australia Meenakshi Coll Engn Dept Elect & Elect Engn Chennai 600078 Tamil Nadu India Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India Jubail Ind Coll Dept Elect & Elect Engn Technol Al Jubail 31961 Jubail Ind City 31961 Saudi Arabia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking-based Crow Search algorithm (FRCSA) is amalgamated with modified artificial bee colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual's best solution (p(best)) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

关键词： Combined heat and power economic dispatch Crow search algorithm artificial bee colony algorithm Adaptive ranking selection Fuzzy logic Sine-Cosine

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Multitude of Objectives Based Optimum Designs of Cylindrical Roller Bearings With Evolutionary Methods

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JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME 2015年第4期137卷 041504-041504页

作者： Tiwari, Rajiv Chandran, Rahul M. P. Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 India

In the design of cylindrical roller bearings (CRBs), a long life is one of the most essential criteria. However, the life of bearings depends on the multitude of factors that includes the fatigue, lubrication, and thermal characteristics in bearings. In the present work, three primary objectives namely, the dynamic capacity, the elastohydrodynamic lubrication (EHL) minimum film-thickness, and the maximum temperature have been optimized, sequentially. Some of these objectives may be contradicting to each other. The optimum bearing design has been attempted by first deriving a constrained nonlinear formulation and then optimizing it with an evolutionary algorithm. Constraint violations study has been performed to have assessment of the effectiveness of each of the constraints. A convergence study has been carried out to ensure the near global optimum point in the design. In terms of the basic dynamic capacity of the bearing, there is an excellent conformity among the optimized and customary bearings. A sensitivity study of various geometric design variables has been performed to see changes in objective functions and results show that geometric variables have hardly any undesirable influence.

关键词： cylindrical roller bearings optimal design artificial bee colony algorithm sensitivity study

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ABC_DE_FP: a novel hybrid algorithm for complex continuous optimisation problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第1期14卷 46-61页

作者： Agarwal, Parul Mehta, Shikha Jaypee Inst Informat & Technol Dept Comp Sci & Engn Noida India

artificial bee colony (ABC) algorithm evolved as one of the efficient swarm intelligence-based algorithm in solving various global optimisation problems. Though numerous variants of ABC are available, algorithm depicts poor convergence rate in many situations. Therefore, maintaining balance between intensification and diversification of an algorithm still needs attention. In this context, a novel hybrid ABC algorithm (ABC_DE_FP) has been developed by integrating FPA and DE in original ABC algorithm. To assess the efficacy of proposed hybrid algorithm, it is primarily compared with contemporary ABC variants such as GABC, IABC and AABC over simple benchmark problems. Thereafter, it is evaluated with respect to original ABC, FPA, hybrid ABC_FP, ABC_DE and ABC_SN over CEC2014 optimisation problems for up to 100 dimensions. Results reveal that proposed algorithm considerably outperforms its counterparts in terms of minimum error value attained and convergence speed for majority of global numerical optimisation functions.

关键词： artificial bee colony algorithm CEC 2014 benchmark functions nature inspired algorithms flower pollination algorithm FPA differential evolution convergence speed complex continuous optimisation problems minimum error value intensification and diversification

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Bernstain-search differential evolution algorithm for numerical function optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 138卷 112831-112831页

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Fac Engn Dept Geomat Engn Kayseri Turkey

The standard Differential Evolution algorithm (sDE) is a stochactic search method commonly used in evolutionary computing. The problem solving success of sDE is highly sensitive to the genetic operators used and the initial values of the parameters of these operators. Since a universal Differential Evolution algorithm (uDE) is not sensitive to the structure and parameter values of the genetic operators used, it is parameter-free in practice and easier to control than sDE. uDE does not need a trial-and-error process when selecting the genetic operators and initial values of intrinsic parameter of related genetic operators to solve the problem, unlike the sDE. Therefore, the use and adaptation of a uDE to solve different types of numerical engineering problems is easy and time-consuming compared to sDE. in this paper, a new uDE, Bernstain-Search Differential Evolution algorithm (BSD), is introduced. BSD is new and easily controllable, simple structured, non-recursive, highly efficient, fast and practically parameter-free uDE. BSD have a too feasible random crossover and mutation process and does not have a control-parameter setting process, contrary to sDE and its improved variants. In this paper, 30 benchmark problems of CEC'2014, 60 classic benchmark problems, image evolution problems for 12 test images and one Triangulated Irregular Network (TIN) refinement problem were used in the experiments performed to investigate the problem solving success of BSD, statistically. Four tested methods (i.e., ABC, JADE, CUCKOO, WDE) were used in the conducted experiments. Problem solving successes of BSD and tested methods were statistically compared by using Wilcoxon Signed Rank Test piecewisely. Results obtained from the performed tests showed that in general, problem solving success of BSD is statistically better than the tested methods that have been used in this paper. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Differential evolution algorithm Cuckoo search algorithm Weighted differential evolution algorithm Image evolution

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An Efficient Universal bee colony Optimization algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2020年第1期27卷 320-332页

作者： Han, Xuming Wang, Yidan Cai, Chen Wang, Limin Hou, Xiuping Wang, Linlin Changchun Univ Technol Sch Comp Sci & Engn Changchun 130012 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Changchun 130012 Peoples R China Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun 130117 Peoples R China

The artificial bee colony algorithm is a global optimization algorithm. The artificial bee colony optimization algorithm is easy to fall into local optimal. We proposed an efficient universal bee colony optimization algorithm (EUBCOA). The algorithm adds the search factor u and the selection strategy of the onlooker bees based on local optimal solution. In order to realize the controllability of algorithm search ability, the search factor u is introduced to improve the global search range and local search range. In the early stage of the iteration, the search scope is expanded and the convergence rate is increased. In the latter part of the iteration, the algorithm uses the selection strategy to improve the algorithm accuracy and convergence rate. We select ten benchmark functions to testify the performance of the algorithm. Experimental results show that the EUBCOA algorithm effectively improves the convergence speed and convergence accuracy of the ABC algorithm.

关键词： artificial bee colony algorithm fitness search factor universal optimization

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Integrated Optimization Approach of Hybrid Flow-Shop Scheduling Based on Process Set

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IEEE ACCESS 2020年 8卷 223782-223796页

作者： Li, Xixing Tang, Hongtao Yang, Zhipeng Wu, Rui Luo, Yabo Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg & Qual Engn Wuhan 430068 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Hubei Key Lab Digital Mfg Wuhan 430070 Peoples R China

Considering that process planning and production scheduling are independent of each other in a hybrid flow-shop, this study categorizes the process route into parallel process-set, batch set and unordered process-set, and builds a multi-objective optimization model to minimize the maximum completion time and the minimum processing cost. An improved artificial bee colony algorithm has been developed to solve the model. A segmented decoding method based on the insertion principle and the release time of the predecessor process is proposed to effectively use the idle time of the machine. A dynamic triggering neighborhood mechanism is introduced to enhance the local searchabilit of the algorithm. Finally, the feasibility and effectiveness of this algorithm to solve such problems are verified via simulation experiments.

关键词： Job shop scheduling Hafnium Batch production systems artificial bee colony algorithm Heuristic algorithms Manufacturing Optimization Process planning production scheduling hybrid flow-shop improved artificial bee colony algorithm

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