Types Of Optimization Methods Research Articles

Dynamic position optimization of submerged array square jet cooling in confined space for chip thermal dissipation

Jet position has a significant influence on the heat transfer characteristics of the chip. Therefore, it is necessary to optimize the jet position to improve its operating performance. Besides, traditional and typical optimization method is to establish abundant experimental/numerical models, which is very complicated and time-consuming, and the accuracy and effectiveness of results still need to be verified. Hence, a dynamic optimization method is proposed in this study and the jet position is directly controlled during the simulation process based on dynamic regulation of user-defined functions, which is directly related to the real-time physical quantities and the user-defined optimization strategy. Results show that compared to traditional jet position, the maximum temperature and temperature dispersion for the optimal jet position decrease by an average of 25.20 °C and 69.88 %, respectively. Furthermore, the optimal jet position efficiently achieves the balance between thermal performance and power consumption. To sum up, compared with manual optimization method, the dynamic optimization method proposed in this study only needs to establish one model and simulate it once to obtain the optimized result. This process is very convenient, high-efficiency and less time-consuming, which will be especially beneficial to effectively enhance the performance of the chip thermal dissipation.

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

Fuzzy logic applied to mutation size in evolutionary strategies

Tuning of algorithm parameters is a complex but very important issue in the design of Evolutionary Algorithms. This paper discusses a new concept of mutation size tuning in Evolutionary Strategies. The proposed algorithm uses data on evolutionary history in earlier generations to tune the mutation size. A Fuzzy Logic Part examines this historical data and tunes the mutation size of individuals to improve the algorithm’s convergence and its resistance to getting stuck in a local optimum. The Fuzzy Logic Part tunes the mutation size and keeps an appropriate relation of algorithm’s exploration and exploitation. The proposed concept is discussed, and several tests on Function Optimization Problems are performed. In tests, we use a set of data and functions with different difficulties recommended in the commonly used benchmarks. The results of experiments suggest that the proposed method is more efficient and resistant to getting stuck in suboptimal solutions. The proposed algorithm has been used in recognizing the type of ultra-high energy cosmic ray particle that initiates the Extensive Air Showers when hit the Earth atmosphere. It could be used for a wide range of similar problems. It is possible that the proposed method could be adapted to other types of optimization methods, inspired by natural evolution, for example, Evolutionary Algorithms.

Reliability-based design optimization using adaptive Kriging-A single-loop strategy and a double-loop one

With the development of the surrogate-assisted Reliability-Based Design Optimization (RBDO) methods in recent decade, efficiency has been continuously improved by various state-of-the-art learning schemes. However, ensuring sufficient accuracy and feasibility at the optimum is still challenging, especially for real scenarios with complex probabilistic constraints and high fidelity. In order to achieve a good balance between efficiency and accuracy and feasibility at the optimum, both a single-loop and a double-loop adaptive Kriging-based RBDO methods are proposed. By directly approximating the probabilistic constraints using Kriging models in the single-loop method, the original RBDO problem is converted into a trivial deterministic optimization, which can be solved by any type of optimization method. The real values of failure probabilities are estimated by Generalized Subset Simulation (GSS) at the initial construction or during each update of the Kriging models. To further raise efficiency in the double-loop adaptive Kriging-based method, GSS is substituted by an inner-loop Kriging-based failure probability estimation, which includes each local enrichment at the limit states corresponding to the active probabilistic constraints based on an initial global enrichment within the augmented reliability space. Once a certain Kriging model meets the requirement during the refining of all, only the others are kept updating until the accuracy of all are accepted. Four examples are used to demonstrate the performance of the proposed two methods.

A Roadside Millimeter-Wave Radar Calibration Method Based on Connected Vehicle Technology

Roadside millimeter-wave radar is one of the most important sensors in roadside perception systems, which have been widely used in intelligent traffic systems. It is important to find an effective calibration procedure for roadside radar to obtain the World Geodetic System-1984 (WGS-84) coordinates of detected targets. However, it is difficult to calibrate roadside millimeter-wave radar safely on public roads without road closures. To solve this problem, this article proposes a roadside millimeter-wave radar calibration method based on connected vehicle technology. First, all the trajectory points of vehicles detected by radar, including the connected vehicle, are collected, and the WGS-84 coordinates of the connected vehicle are obtained by vehicle-to-everything (V2X) communication with the roadside unit. Then, the trajectory points of vehicles are clustered with the density-based spatial clustering of applications with noise (DBSCAN) method, and the trajectory of the connected vehicle in the radar coordinate system is identified with the velocity-matching method automatically. The data pairs of the connected vehicle in the radar coordinate system and WGS-84 coordinate system are obtained with time synchronization. Finally, the calibration parameters are solved with five different types of optimization methods. We verify these methods on the beltway in Changsha. The results show that the improved pseudo inverse method (IPIM) and PIM achieve better performance than the extrinsic calibration method (ECM), rotate coordinate method (RCM), and improved RCM, and they can meet lane-level positioning requirements for V2X applications. This study provides an economical and effective way to solve the calibration problem for roadside millimeter-wave radar in an engineering project.

Design and Implementation of an 18-kW 500-kHz 98.8% Efficiency High-Density Battery Charger With Partial Power Processing

The demand for high-density, high-efficiency bidirectional battery chargers is driven by the fast development of energy storage system in renewable energy system, microgrid, and transportation electrification. Isolated dc–dc converter that interfaces a battery with a variable voltage range is one of the critical components. Input-parallel output-series (IPOS) partial power (PP) converter is considered a promising high-efficiency, high-density solution because only a fraction of power is processed via multistage converters to regulate the output voltage. However, due to the tight coupling between two dc transformers (DCXs), the design of PP converter is complicated. To solve this issue, an overall design procedure for a bidirectional soft-switching resonant-type PP converter is proposed. In the parameters design part, a decoupled design method is proposed to simplify the DCXs design. With this method, two DCXs can be designed separately, and a typical optimization method can be easily applied. As for the hardware design part, to minimize the ac loop inductance and resistance, a two-direction (2-D) flux cancellation method and an “intraleaving” winding structure are proposed for circuit layout and high-frequency (HF) transformer to obtain high operation efficiency and power density. Finally, the whole design procedure is verified by an 18-kW-rated, 25-kW peak, prototype operating at 500 kHz. The realized PP converter features a peak efficiency of 98. 8% and a power density of 142 W/in3. This article is accompanied by two videos demonstrating the dynamic voltage regulation test and the efficiency measurement.

Integrated fabrication process with multiple optimized factors for high power density of IPMC actuator

ABSTRACT Ionic polymer-metal composites (IPMCs) are typical smart materials that are commonly used in bionic applications, including soft robots, bionic flapping aircraft, and bionic fish. However, their low output force seriously limits device performance. Stacking of multiple IPMC actuators to improve the overall performance of soft actuators is a strategy that is used in practical applications. Under the energy dissipation condition in the IPMC stacking structure, if each single IPMC in the structure has high power density, the structure will produce excellent performance with high efficiency that can greatly promote wider application of IPMC actuators. To meet this requirement, a method for fabrication process integration with multiple optimized factors was used to obtain IPMC materials in this paper. Carbon nanotube (CNT) doping, isopropyl alcohol-assisted plating, and hot pressing with a mesoscopic structural mold were selected as typical optimization methods for process integration and were initially investigated separately to determine the optimal process parameters. By combining the best process parameters in an integrated process, the IPMC treated by isopropyl alcohol-assisted plating and CNT doping process (No. AC7) showed excellent actuation performance and high work density (~9.71/12.36 gf, ~14.93/31.89 kJ/m3 under 3/4 VDC). The enhanced performance meets the requirements for practical bionic applications.

Determination of Broadband Complex EM Parameters of Powdered Materials: 1. MCMC‐Based Two‐Port Transmission Line Measurements

AbstractWe propose a Bayesian Markov Chain Monte Carlo parameter estimation technique to determine frequency‐dependent complex dielectric permittivity and permeability of powdered planetary regolith analog materials from coaxial transmission line measurements. The technique uses either a flat (non‐dispersive) or a generalized dielectric response model to simultaneously determine the complex permittivity and permeability from measured scattering parameters. By exploring the parameter space, the technique reduces the dependence on good initial model parameters required by typical optimization methods. The technique can be used with either two‐port measurements or shorted measurements, where an additional one‐port shorted sample is measured. We find the technique is able to accurately determine electromagnetic parameters of low‐loss, high dielectric, and magnetic materials. The technique is also robust to half‐wavelength resonances in the sample as well as the presence of higher order modes.

Advances in Sparrow Search Algorithm: A Comprehensive Survey.

Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

Optimization methods are considered as one of the highly developed areas in ArtificialIntelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)has encouraged researchers to develop other methods that can obtain better performance outcomes and tobe more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH)are some of those methods that showed a great success in different applications in the last few years. In thispaper, we propose a comparative study of using different optimization methods including KH and GWOin order to solve the problem of document feature selection for the classification problem. These methodsare used to model the feature selection problem as a typical optimization method. Due to the complexityand the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques tomake the judgement of which features subset that is optimal to enhance the performance of classificationof text documents. The test results showed the superiority of GWO over the other counterparts using thespecified evaluation measures.

Optimization methods for large-scale vaccine supply chains: a rapid review.

Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

Online Estimation of the Mechanical Parameters of a Wind Turbine with Doubly Fed Induction Generator by Utilizing Turbulence Excitations

In this paper, a new method using wind turbulence excitation is proposed to estimate the parameters of the mechanical system (drivetrain and pitch angle controller) in a Doubly Fed Induction Generator (DFIG) Wind Turbine (WT). Firstly, simulations were carried out for a DFIG WT under turbulence excitations. The spectral contents of the responses imply that the transients of the electrical system (generator and converter), which are much faster than those of the mechanical system, can be neglected when estimating the mechanical parameters. Based on this, a simplified model related to the mechanical system of the DFIG WT was derived by applying the model reduction technique. Secondly, the parameter sensitivity of Power Spectral Density (PSD) was used to quantify the impacts of individual parameters on the dynamics of the mechanical system, and the influential parameters were selected on the basis of the sensitivity results. Finally, a weighted least-squares optimization problem, which is suitable for a system with close oscillation modes, was formulated for parameter estimation. The estimation results based on two different types of optimization methods were compared, and their estimation accuracies validate the effectiveness of the proposed method.

A review of optimization methods for computation offloading in edge computing networks

Handling the massive amount of data generated by Smart Mobile Devices (SMDs) is a challenging computational problem. Edge Computing is an emerging computation paradigm that is employed to conquer this problem. It can bring computation power closer to the end devices to reduce their computation latency and energy consumption. Therefore, this paradigm increases the computational ability of SMDs by collaboration with edge servers. This is achieved by computation offloading from the mobile devices to the edge nodes or servers. However, not all applications benefit from computation offloading, which is only suitable for certain types of tasks. Task properties, SMD capability, wireless channel state, and other factors must be counted when making computation offloading decisions. Hence, optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks. In this paper, we review six types of optimization methods - they are Lyapunov optimization, convex optimization, heuristic techniques, game theory, machine learning, and others. For each type, we focus on the objective functions, application areas, types of offloading methods, evaluation methods, as well as the time complexity of the proposed algorithms. We discuss a few research problems that are still open. Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.

Nonlinear model-based cardiac arrhythmia diagnosis using the optimization-based inverse problem solution.

This study investigates a nonlinear model-based feature extraction approach for the accurate classification of four types of heartbeats. The features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution. In the model-based methods, high feature extraction time is a crucial issue. In order to reduce the feature extraction time, a new structure was employed in the optimization algorithms. Using the proposed structure has considerably increased the speed of feature extraction. In the following, the effectiveness of two types of optimization methods (genetic algorithm and particle swarm optimization) and the McSharry ECG model has been studied and compared in terms of speed and accuracy of diagnosis. In the classification section, the adaptive neuro-fuzzy inference system and fuzzy c-mean clustering methods, along with the principal component analysis data reduction method, have been utilized. The obtained results reveal that using an adaptive neuro-fuzzy inference system with data obtained from particle swarm optimization will have the shortest process time and the best diagnosis, with a mean accuracy of 99% and a mean sensitivity of 99.11%.

A data-enhanced distributionally robust optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty

With growing penetrations of wind power in electricity systems, the coordinated dispatch of integrated electricity and natural gas systems is becoming a popular research topic. Distributionally robust optimization can cope with the wind uncertainty of integrated electricity and natural gas systems by providing optimal solutions for the worst-case probability distribution. However, limited historical wind data hinder the estimation of worst-case probability distribution. As a breakthrough in artificial intelligence, generative adversarial networks can be established to approximate a complex uncertain probability distribution from raw data and generate realistic data subject to the identical distribution. This paper proposes a data-driven optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty, whose probability distribution is free. Based on limited historical data, the data-driven generative adversarial network generates artificial wind power data, which helps to improve the estimation of worst-case probability distribution in distributionally robust optimization. Moreover, the robustness of optimization solutions can be adjusted cost-effectively by controlling the auxiliary data number. In a case study, optimization solutions of the proposed method are shown to achieve a lower probability of chance constraint violation at a nearly negligible cost increase compared with those from four typical optimization methods.

A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

Optimization methods are considered as one of the highly developed areas in Artificial Intelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) has encouraged researchers to develop other methods that can obtain better performance outcomes and to be more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH) are some of those methods that showed a great success in different applications in the last few years. In this paper, we propose a comparative study of using different optimization methods including KH and GWO in order to solve the problem of document feature selection for the classification problem. These methods are used to model the feature selection problem as a typical optimization method. Due to the complexity and the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques to make the judgement of which features subset that is optimal to enhance the performance of classification of text documents. The test results showed the superiority of GWO over the other counterparts using the specified evaluation measures.

Estimating the Social Gap With a Game Theory Model of Lane Changing

Changing lanes is a commonly-used technique for drivers to either overtake slow-moving cars or enter/exit highway ramps. Optional lane changes may save drivers travel time but increase the risk of collision with others. Drivers make such decisions based on experience and emotion rather than analysis, and thus may fail to select the best solution while in a dynamic state of flux. Unlike human drivers, autonomous vehicles can systematically analyze their surroundings and make real-time decisions accordingly. This paper develops a game theory-based lane-changing model by comparing two types of optimization methods. To realize our expectations, we need to first investigate the payoff function of drivers in discretionary lane-changing maneuvers and then quantify it in an equation of costs that trades-off safety and time-saving. After the evaluation for each alternative strategy combination, the results show that there exists a social gap in the discretionary lane-changing game. To deal with that problem, we provide some suggestions for future policy as well as autonomous vehicle controller designs, offering solutions to reduce the impact of disturbances and crashes caused by inappropriate lane changes, and also, inspire further research about more complex cases.

Estimation and statistical inferences of variance components in the analysis of single-case experimental design using multilevel modeling.

Multilevel models (MLMs) can be used to examine treatment heterogeneity in single-case experimental designs (SCEDs). With small sample sizes, common issues for estimating between-case variance components in MLMs include nonpositive definite matrix, biased estimates, misspecification of covariance structures, and invalid Wald tests for variance components with bounded distributions. To address these issues, unconstrained optimization, model selection procedure based on parametric bootstrap, and restricted likelihood ratio test (RLRT)-based procedure are introduced. Using simulation studies, we compared the performance of two types of optimization methods (constrained vs. unconstrained) when the covariance structures are correctly specified or misspecified. We also examined the performance of a model selection procedure to obtain the optimal covariance structure. The results showed that the unconstrained optimization can avoid nonpositive definite issues to a great extent without a compromise in model convergence. The misspecification of covariance structures would cause biased estimates, especially with small between case variance components. However, the model selection procedure was found to attenuate the magnitude of bias. A practical guideline was generated for empirical researchers in SCEDs, providing conditions under which trustworthy point and interval estimates can be obtained for between-case variance components in MLMs, as well as the conditions under which the RLRT-based procedure can produce acceptable empirical type I error rate and power.

PERFORMANCE ANALYSIS OF OPTIMIZATION METHODS FOR SOLVING TRAVELING SALESMAN PROBLEM

The subject of this research is distance and time of several city tour problems which known as traveling salesman problem (tsp). The goal is to find out the gaps of distance and time between two types of optimization methods in traveling salesman problem: exact and approximate. Exact method yields optimal solution but spends more time when the number of cities is increasing and approximate method yields near optimal solution even optimal but spends less time than exact methods. The task in this study is to identify and formulate each algorithm for each method, then to run each algorithm with the same input and to get the research output: total distance, and the last to compare both methods: advantage and limitation. Methods used are Brute Force (BF) and Branch and Bound (B&B) algorithms which are categorized as exact methods are compared with Artificial Bee Colony (ABC), Tabu Search (TS) and Simulated Annealing (SA) algorithms which are categorized as approximate methods or known as a heuristics method. These three approximate methods are chosen because they are effective algorithms, easy to implement and provide good solutions for combinatorial optimization problems. Exact and approximate algorithms are tested in several sizes of city tour problems: 6, 9, 10, 16, 17, 25, 42, and 58 cities. 17, 42 and 58 cities are derived from tsplib: a library of sample instances for tsp; and others are taken from big cities in Java (West, Central, East) island. All of the algorithms are run by MATLAB program. The results show that exact method is better in time performance for problem size less than 25 cities and both exact and approximate methods yield optimal solution. For problem sizes that have more than 25 cities, approximate method – Artificial Bee Colony (ABC) yields better time which is approximately 37% less than exact and deviates 0.0197% for distance from exact method. The conclusion is to apply exact method for problem size that is less than 25 cities and approximate method for problem size that is more than 25 cities. The gap of time will be increasing between two methods when sample size becomes larger.

Optimization to optimization (OtoO): optimize monarchy butterfly method with stochastics multi-parameter divergence method for benchmark functions and load frequency control

Optimization to optimization (OtoO) approach is proposed in this study. It aims to increase an optimization algorithm performance. OtoO approach has two types of optimization methods. First is essential algorithm, which is used for solution of the basic problem. Second is auxiliary algorithm that adjusted the parameters of the essential algorithm. In this study, the monarchy butterfly optimization (MBO) method and stochastic multi-parameter divergence optimization (SMDO) method were defined as essential algorithm and auxiliary algorithm, respectively. Constant parameters of the MBO method that affect performance (Keep, Max. Step Size, period and BAR) are primarily optimized on benchmark functions with the SMDO algorithm, and results are compared with each other and classical MBO, ABC (Artificial Bee Colony), ACO (Ant Colony), BBO (Biogeography-based), SGA (Simple Genetic) and DE (Differential Evolution) algorithms. In addition, OtoO approach is also tried via composite benchmark functions. In addition, PI and PID controllers were designed for the load frequency control of a hybrid power system. Results are compared with the FA (Firefly Algorithm) and GA (Genetic Algorithm) results. Results demonstrate that the performance of algorithms can be increased without disrupting the basic philosophy of algorithms and hybridizing algorithms with the proposed OtoO approach via benchmark functions and engineering problems.