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作者： Mahadik, Tejas Prabhakar State University of New York at Binghamton

学位级别：M.S., Master of Science/Master of Surgery

Drones, or unmanned aerial vehicles (UAV), can significantly reduce the cost and time required for the inventory counting that takes place daily, weekly, or monthly to maintain the record of the current inventory levels. This thesis introduces an approach for finding the optimal drone path planning in the dynamic warehouse environment. This study aims to minimize energy consumption and simultaneously scan the inventory levels. Earlier, various approaches are proposed, which use multiple sensors (e.g., GPS, IMU) to increase the efficiency of localization and mapping of drones but have been proved to be ineffective in indoor environments. A 3-D grid cell representation of the static environment is carried out with each cell assigned to an ArUco marker. These ArUco markers consist of real-world coordinates that can be captured with the primary cameras present on the drone. Various state-of-the-art evolutionary optimization algorithms (EOA) such as Simulated Annealing (SA), Ant Colony optimization (ACO), and Particle Swarm optimization (PSO) and unsupervised machine learning techniques like Self-Organizing Maps (SOM) are used, and a hybrid version of each of them is developed to obtain the set of non-dominated solutions, which are found by searching the available neighborhood cells. The results show the effectiveness of applied hybrid algorithms in solving the various test cases and the SOM-PSO version produces better results when compared to SOM-ACO and SOM-SA versions. It was observed that SOM-PSO was better than the other two hybrid algorithms and produced results with an average of 1.89% optimality gap for the benchmark TSP dataset and an average of 6.73% for the warehouse test case dataset.

关键词： Drones evolutionary optimization algorithms Inventory management Machine learning Path planning Warehouse 4.0

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Regression line shifting mechanism for analyzing evolutionary optimization algorithms

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SOFT COMPUTING 2017年第21期21卷 6237-6252页

作者： Biswas, Anupam Biswas, Bhaskar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

This work introduces a novel methodology to perform the comparative analysis of evolutionary optimization algorithms. The methodology relies simply on linear regression and quantile-quantile plots. The methodology is extrapolated as the one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, many-to-many comparison, i.e., ranking of algorithms is done only in terms of solution quality. The proposed method is capable of ranking algorithms in terms of both solution quality and convergence rate. Method is analyzed with well-established algorithms and real data obtained from 25 benchmark functions.

关键词： evolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis

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Optimal design of a honeycomb core composite sandwich panel using evolutionary optimization algorithms

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COMPOSITE STRUCTURES 2016年 139卷 254-262页

作者： Gholami, Meghdad Alashti, Reza Akbari Fathi, Alireza Babol Univ Technol Dept Mech Engn Babol Sar Iran

The present paper deals with the optimal design of a composite sandwich panel with honeycomb core structure using particle swarm optimization (PSO) technique. The face sheets of sandwich panel are considered to be thin and the sandwich panel is subjected to a uniformly distributed normal load. The Navier type solution and the closed-form expressions for the macroscopic in-plane elastic constants of the honeycombs are used to predict the deflection of the panel. Also, the maximum stress in the composite sandwich panel is determined using the macroscopic stress-strain relation of the honeycomb core and the plate. Niching Memetic PSO (NMPSO) and Locally Informed Particle Swarm (LIPS) variants of PSO are examined to minimize the weight of the panel. Numerical results showed that the optimal geometry of the honeycomb cell has this property that its radius and thickness converge to their lower bounds while its length converges to its upper bound. This means that to have an optimal panel, each cell should have the allowable minimum cross section (highest number of cells) and maximum allowable length. Moreover the effects of panel aspect ratio, cell width and applied load were examined. Also, the numerical results confirm the efficiency and effectiveness of the NMPSO in finding optimal solution on the constrained and unconstrained objective functions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Honeycomb Sandwich panel Particle swarm optimization Niching Memetic PSO evolutionary optimization algorithms Locally Informed Particle Swarm (LIPS)

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evolutionary based Pareto optimization algorithms for bi-objective PV array reconfiguration under partial shading conditions

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ENERGY CONVERSION AND MANAGEMENT 2022年 271卷

作者： Zhang, Xiaoshun Meng, Die Li, Wenji Yu, Tao Fan, Zhun Hao, Zhifeng Northeastern Univ Foshan Grad Sch Innovat Foshan 528311 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shantou Univ Coll Engn Shantou 515063 Peoples R China South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China Key Lab Digital Signal & Image Proc Guangdong Prov Shantou 515063 Peoples R China

During the daily operation of photovoltaic array, it easily faces the partial shading conditions resulted from the cloud shadow, dropping dust, etc. It will directly cause a lifetime reduction and a generation efficiency decre-ment for the photovoltaic array. To weaken the negative influence of partial shading condition, one of the most favoured ways is the photovoltaic array reconfiguration. However, the conventional photovoltaic array recon-figuration only aims to maximize the power output, which did not consider the lifetime and control complexity of switching devices. To fill up this gap, this paper constructs a new bi-objective optimization of photovoltaic array reconfiguration, which attempts to simultaneously maximize the output power and minimize the switching number. Consequently, it can dramatically reduce the switching control complexity while improving the gen-eration efficiency, while the operation life of the switching devices can be lengthened. In order to find a high -quality Pareto optimal reconfiguration schemes, six frequently-used evolutionary multi-objective optimization algorithms are employed to solve this bi-objective optimization. The effectiveness of bi-objective optimization of photovoltaic array reconfiguration is tested on three scales of total-cross-tied photovoltaic arrays under four partial shading patterns. The simulation results show that the maximum power increment by the proposed technique is up to 26.6% against to that without optimization, while the average switch number decrement is up to 31.1% compared with the single-objective optimization algorithms.

关键词： PV array reconfiguration Bi-objective optimization evolutionary optimization algorithms Partial shading condition

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evolutionary optimization for Active Debris Removal Mission Planning

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IEEE ACCESS 2023年 11卷 41019-41033页

作者： Zona, Danilo Zavoli, Alessandro Federici, Lorenzo Avanzini, Giulio Univ Salento Dept Engn Innovat I-73100 Lecce Italy Sapienza Univ Rome Dept Mech & Aerosp Engn I-00184 Rome Italy Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA

Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region of space. Such a problem is known to be computationally demanding and the present paper provides a technique for preliminary mission planning based on a novel evolutionary optimization algorithm, which identifies the best sequence of debris to be captured and/or deorbited. A permutation-based encoding is introduced, which may handle multiple spacecraft trajectories. An original archipelago structure is also adopted for improving algorithm capabilities to explore the search space. As a further contribution, several crossover and mutation operators and migration schemes are tested in order to identify the best set of algorithm parameters for the considered class of optimization problems. The algorithm is numerically tested for a fictitious cloud of debris in the neighborhood of Sun-synchronous orbits, including cases with multiple chasers.

关键词： Costs Orbits Genetic algorithms optimization Space vehicles Planning Space debris Active debris removal evolutionary optimization algorithms space mission design

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Practical Application of Fractional-Order PID Controller based on evolutionary optimization Approach for a Magnetic Levitation System

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IETE JOURNAL OF RESEARCH 2023年第11期69卷 8168-8192页

作者： Dey, Soham Banerjee, Subrata Dey, Jayati Dr BC Roy Engn Coll Dept Elect Engn Durgapur India Natl Inst Technol Dept Elect Engn Durgapur India

This article proposes a state-of-the-art design procedure of integer-order PID (IOPID) and fractional-order PID (FOPID) controller applied to a well-established and diversified engineering application of the Magnetic Levitation System (Maglev). Controller design and implementation for the Maglev system are quite complicated and difficult since the system dynamics exhibits non-linearity with a wide variation of operating points. Also, the system is highly unstable which rules out the possibility to accomplish conventional tuning techniques. Thus in this work, the controller tuning methodology is framed as a complex optimization problem by incorporating a new transient specification-based objective function. For designing and tuning of proposed controller parameters, modern meta-heuristic and evolutionary optimization algorithms are deployed;those are Bird Swarm Algorithm, Elephant Herding optimization, Equilibrium optimizer and Grey Wolf optimization. The software and hardware results demonstrate that FOPID controllers yield better time-domain and frequency-domain performance specifications and exhibit excellent reference tracking capability than IOPID controllers. The performance robustness of the proposed controllers is greatly enhanced subjected to a vast range of parametric uncertainties along with a significant minimization of the control effort.

关键词： Equilibrium optimizer evolutionary optimization algorithms FOPID Intelligent control system design MAGLEV system Real-time control Robustness analysis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第3期236卷 1550-1570页

作者： Sardashti, A. Daniali, H. M. Varedi-Koulaei, S. M. Babol Univ Technol Dept Mech Engn Babol Iran Shahrood Univ Technol Dept Mech & Mechatron Engn POB 36199-95161 Shahrood Iran

This paper presents a novel methodology for path generation synthesis of the four-bar mechanism. A new objective function for the path generation synthesis problem, namely, the Geometrical Similarity Error Function (GSEF), is introduced. Indeed, GSEF assesses the similarity between generated and desired paths, and its number of design variables is less than those in the other synthesis methods. Then, using an Innovative Adaptive Algorithm (IAA), some operators are utilized for matching two similar paths. GSEF-IAA methodology has some significant advantages over the reported synthesis methods. The method is fast, takes much less CPU time, and saves a large amount of computer memory. Four path generation problems are solved using GSEF-IAA, and the results are compared with those of previous methods using some well-known optimization algorithms to demonstrate the efficiency of GSEF-IAA methodology.

关键词： Four-bar mechanism path generation synthesis Geometrical Similarity Error Function Innovative Adaptive Algorithm evolutionary optimization algorithms

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Silicon on Insulator C-VTFET Based Design of low Complexity Sparse Quadrature Mirror Filter Using Differential Search Algorithm

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SILICON 2022年第17期14卷 11545-11560页

作者： Singh, Hitendra Dwivedi, Atul Kumar Nagaria, Deepak Bundelkhand Inst Engn & Technol Dept Elect & Commun Engn Jhansi 284128 Uttar Pradesh India

In this paper, a 60 nm Complementary-Vertical TFET (C-VTFET) is designed using silicon on insulation technology is implemented for low power quadrature mirror filter design for different search algorithm using silvaco TCAD simulation. Various advantages of SOI have been incorporated for the realization of low-voltage with low power (LVLP) for VLSI design digital circuits. To prevent the losses of the Lattice mismatch structure, gate staking of high k -dielectric (HfO2) material with SiO2 was employed using equivalent oxide thickness method. Various engineering method incorporated to optimized the Drain-Voltage characteristics. N-Type and P-Type VTFET is considered using the mixed mode technique. The SiGe layer is employed to enhance the tunneling method of by reducing the bandgap firm 1.1 eV to 0.7 eV. The highest I-ON current and minimum I(OFF )current is reported for the proposed device is I (3.62 x 10(-4) A/mu m) and (1.58 x 10(-18) A/mu m) respectively. The ON/OFF current ratio out recorded as similar to 10(13) respectively. The reconstruction quadrature mirror filter architecture in this manuscript is computationally efficient and virtually flawless. Sparsity among the coefficients is introduced to reduce the number of multipliers and adders required to construct prototype the filter H-0(Z), resulting in a reduction in computational complexity. A well-known population-based evolutionary optimization differential search algorithm is used to optimized the objective function. However, Levy's differential search method is also advocated due to sluggish exploitation of traditional differential search algorithm. The suggested algorithm's effectiveness is evaluated by comparing it to other evolutionary optimization techniques. The results demonstrate that the proposed technique outperforms previously published evolutionary optimization algorithms. The above analysis validated the proposed device are well incorporated for the execution to design low power filter design

关键词： Silicon-on-insulator complementary VTFET Heterojunction Differential search algorithm evolutionary optimization algorithms Quadrature mirror filter

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An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self -organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning timeconsuming, and local minimum trapping, especially when using gradient -based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self -adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self -adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

关键词： Artificial Neural Networks Multilayer perceptron network Hyperparameters Metaheuristic algorithm Swarm intelligence algorithm evolutionary optimization algorithms

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Secure Scheduling of Scientific Workflows in Cloud 12

Secure Scheduling of Scientific Workflows in Cloud

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12th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Roy, Shubhro Ramamurthy, Arun Pawar, Anand Gharote, Mangesh Lodha, Sachin TCS Res Tata Consultancy Serv Gurgaon Haryana India Indian Inst Informat Technol Pune Maharashtra India TCS Res Gurgaon Haryana India

ISBN: (纸本)9789897585708

Scheduling of tasks in scientific workflow has been challenging due to heterogeneous and interdependent tasks in workflow. The scheduling involves selection of different types of virtual machines (VM) belonging to different instance series (computing, memory, storage) to minimize the overall execution cost and time (makespan). Apart from VM selection, selection of security services (such as authentication, integrity, confidentiality) is critical. In this paper, we propose OptReUse - a workflow schedule generation algorithm for efficient reuse of VMs. Our approach of OptReUse algorithm along with combinatorial optimization approach gives lower cost of scheduling compared to the prior art without incurring delay in the makespan. Further, we enhance the security model by accurate estimation of risks. Our experiments using standard scientific workflows demonstrate that the proposed method gives lower costs compared to the prior VM resource utilization methods.

关键词： Scientific Workflow Scheduling Cloud Computing Security in Cloud Task Scheduling evolutionary optimization algorithms

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