Heuristic Optimization Algorithm Research Articles

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.

Optimal Control of Automatic Voltage Regulator System with Coronavirus Herd Immunity Optimizer Algorithm-Based PID plus Second Order Derivative Controller

Optimal control of the Automatic Voltage Regulator (AVR) system can improve the system behavior with the optimal parameters obtained based on optimization. The design of proposed Proportional Integral Derivative (PID) and Proportional Integral Derivative Plus Second Order Derivative (PIDD2) controllers are stated as an optimization problem including objective and constraint. The optimization problem is solved by using Coronavirus herd immunity optimizer (CHIO) algorithm to find the best controller parameters. In this paper, optimal design of PID and PIDD2 controllers for AVR system are presented with different objectives. The optimal design of controllers modeled as an optimization problem is solved with the help of the CHIO Algorithm. CHIO is inspired herd immunity against COVID-19 disease by social distancing. The performances of CHIO-based controllers in AVR system are compared with some well-known algorithms. Also, the obtained results demonstrate that the CHIO algorithm yields the least objective value in comparison with the other algorithms. When the results of the proposed approach are compared to those of some modern heuristic optimization algorithms, such as the Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, Artificial Bee Colony (ABC) algorithm etc. and the superiority of the proposed approach is demonstrated.

Aquila Optimization Algorithm for Wind Energy Potential Assessment Relying on Weibull Parameters Estimation

Statistical distribution approaches have been developed to describe wind data due to the intermittent and unpredictable nature of wind speed. The Weibull distribution with two parameters is thought to be the most accurate distribution for modeling wind data. This study seeks wind energy assessment via searching for the optimal estimation of the Weibull parameters. For this target, analytical and heuristic methods are investigated. The analytical methods involve the maximum likelihood, moment, energy pattern factor, and empirical methods, while the heuristic optimization algorithms include particle warm optimization and the Aquila optimizer (AO). Both analytical and heuristic methods are assessed together to fit the probability density function of wind data. In addition, nine models are submitted to find the most appropriate model to represent wind energy production. The error between actual and estimated wind energy density is computed to the model for each study site which has less error of energy. The fit test is performed with real data for the Zafarana and Shark El-Ouinate sites in Egypt for a year. Additionally, different indicators of fitness properties are assessed, such as the root mean square error, determination coefficient (R2), mean absolute error, and wind production deviation. The simulation results declare that the proposed AO optimization algorithm offers greater accuracy than several optimization algorithms in the literature for estimating the Weibull parameters. Furthermore, statistical analysis of the compared methods demonstrates the high stability of the AO algorithm. Thus, the proposed AO has greater accuracy and more stability in the obtained outcomes for Weibull parameters and wind energy calculations.

Improved Harris hawks optimization for non-convex function optimization and design optimization problems

Harris hawks optimization (HHO) is a nature-inspired algorithm. It has the advantages of very few parameters, a simple structure, fast convergence and strong local search capability. The main drawback of the Harris hawks optimization is that it can easily fall into a local optimum. To solve this problem, a novel mutant strategy based on Brownian motion is proposed to combine with the original HHO. This mutant strategy is driven by exploiting the randomness of Brownian motion and does not require location information between populations and user-set parameters. As a result, it can guide the algorithm to better jump out of the local optimum trap. To verify the performance of the proposed algorithm, numerical experiments are carried out to compare the proposed algorithm with heuristic optimization algorithms for 54 non-convex functions and two classic engineering design problems. The results show that our algorithm not only escapes the local optimum trap, but also has better robustness and convergence.

System response modeling of HMCVT for tractors and the comparative research on system identification methods

The HMCVT (Hydro-mechanical Continuously Variable Transmission) is widely applied in agricultural and engineering machinery such as tractors. Getting a correct system response model is the key to HMCVT control strategy implementation and controller design. The study decomposes and analyzes the HMCVT system and builds response models. The study proposes to decompose the HMCVT system into three dynamic response stages including the electronic proportional pressure relief valve response, the variable plunger displacement response and the pump-motor system speed ratio response, and builds the response models of three stages respectively and then obtains the general form of transfer function of HMCVT system using a tandem method. The study proposes a system identification method based on the prior-model-heuristic-intelligent-optimization algorithm, and makes a comparative analysis between the classical system identification method (the area method) and the three heuristic intelligent optimization algorithms (the FI-SA, the I-SA and the I-AFSA) in terms of system identification result. The study compares the results of identification methods through a step response test of a model of HMCVT for tractors, and verifies the correctness of the transfer function model of HMCVT system proposed. Research results show that the general form of HMCVT system response model should be a 4-order denominator 1-order numerator transfer function model (the mean identification precision of 15 groups of measurement tests is about 0.9849). The area method is affected by the sampling frequency in the response measurement and requires a complete response process measured. The system identification based on heuristic intelligent optimization algorithm has low dependence on sampling frequency and time consumed and high precision. According to the results of the calculation example in the study, the coefficient of determination about the identified model is 1 under the measurement frequency of 4 Hz and the measurement duration of 4 s by using the FI-SA, the I-SA, and the I-AFSA. The precision is improved by 4.93 % compared with using the area method. According to the actual test results, the coefficients of determination about the FI-SA and the area method are 0.9939 and 0.9834, respectively. Comparing the three heuristic intelligent optimization algorithms, the FI-SA proposed has the fastest identification speed. The results of 20 groups measurement tests show that the average iteration number of FI-SA are 43.58 % and 17.95 % lower than those of I-SA and I-AFSA, and the average time consumption of FI-SA is 43.36 % and 84.76 % lower than that of I-SA and I-AFSA.

An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting

During the global fight against the novel coronavirus pneumonia (COVID-19) epidemic, accurate outbreak trend forecasting has become vital for outbreak prevention and control. Effective COVID-19 outbreak trend prediction remains a complex and challenging issue owing to the significant fluctuations in the COVID-19 data series. Most previous studies have limitations only using individual forecasting methods for outbreak modeling, ignoring the combination of the advantages of different prediction methods, which may lead to insufficient results. Therefore, this paper develops a novel ensemble paradigm based on multiple neural networks and a novel heuristic optimization algorithm. First, a new hybrid sine cosine algorithm-whale optimization algorithm (SCWOA) is exercised on 15 benchmark tests. Second, four neural networks are used as predictors for the COVID-19 outbreak forecasting. Each predictor is given a weight, and the proposed SCWOA is used to optimize the best matching weights of the ensemble model. The daily COVID-19 series collected from three of the most-affected countries were taken as the test cases. The experimental results demonstrate that different neural network models have different performances in various complex epidemic prediction scenarios. The SCWOA-based ensemble model can outperform all comparable models with its high accuracy and robustness.

A class of PSO-tuned controllers in Lorenz chaotic system

This paper considers the optimal robust control of chaotic systems subject to parameter perturbations and measurement noise. Three novel dynamic tracking controllers are designed herein to suppress the chaotic behaviour in Lorenz systems under practical constraints. The dynamic controllers allow for extra improvements towards optimal and robust tracking but their design is challenging in the chaotic system. Given a single measured state only, the single-state feedback controllers are optimally tuned using high-performance heuristic Particle Swarm Optimization (PSO) algorithm with a constrained multi-objective function focusing mainly on the Integral Absolute Error (IAE). The controlled system stability is ensured through Routh–Hurwitz stability criteria where control parameters are further tuned using a metaheuristic PSO algorithm to ensure faster transients with minimum errors when tracking desired state trajectories and also to ensure robustness to parameter variations and external disturbances in the chaotic system. Different designs are tested through extensive simulations and comparisons where the proposed PSO-tuned PD-tracking controller is proved superior for not only suppressing the chaotic behaviour and reducing the transients but also for their high robustness to parameter uncertainties and external disturbances.

Combined optimization of connected and automated vehicle distribution and ramp control for weaving scenario

The traffic in the weaving region is subject to lane-changing turbulence, which results in a significant impact on traffic safety, reduces traffic efficiency, and creates traffic bottlenecks. To mitigate the negative effect of the weaving, vehicles are distributed in each segment located inside and upstream of the weaving region by a lane-changing advisory using Connected Autonomous Vehicle (CAV) technology, called segment-base lane-changing control (SLDC), and a heuristic optimization algorithm is applied to obtain the proportion of CAVs to be assigned to each segment. Further, to decrease volume variation on the mainline, the ramp metering (RM) control strategy is coordinated and applied to ensure the lane-changing process in the weaving region. Conditions of different traffic demands are analyzed using the cellular automata traffic simulation based on a typical one-sided ramp motorway weaving region. Results indicated that the proposed CAV distribution algorithm and SLDC-RM coordination control strategy could improve traffic efficiency and reduce traffic conflicts. The coordination control is better than SLDC when the occupancy of the weaving region reaches 25 veh/km/ln, also the effects with different proportions of connected vehicles are discussed.

Modelling and heuristically solving many-to-many heterogeneous vehicle routing problem with cross-docking and two-dimensional loading constraints

In light of the existing practical applications of the two-dimensional loading on vehicle scheduling and many-to-many supply-demand relationships between suppliers and customers, we address a many-to-many heterogeneous vehicle routing problem with cross-docking and two-dimensional loading constraints (or the heterogeneous 2L-MVRPCD). The newly proposed problem can be regarded as a generalized problem of the many-to-many vehicle routing with cross-docking (MVRPCD) and capacitated vehicle routing problem with two-dimensional loading constraints (2L-CVRP). To solve small-scale 2L-MVRPCDs, a mixed integer linear programming (MILP) model is developed, whereas two hybrid optimization heuristic algorithms are proposed to solve large-scale 2L-MVRPCDs. The first heuristic incorporates a basic adaptive large neighborhood search (ALNS) algorithm and a new best-fit-skyline (BFS) packing heuristic, while the second heuristic, a Tabu-based ALNS (ALNS/TS), extends the first one by embedding an insert-tabu strategy to enhance the intensity and diversity of search. Wide-ranging instances with various many-to-many supply-demand scenarios and different loading configurations are employed to verify the efficacy of the proposed MILP model and two heuristics. Numerical results show that small-scale heterogeneous 2L-MVRPCD instances with and without the rotation constraints can be solved to optimality by commercial solvers, and the proposed heuristics can achieve high-quality solutions within a reasonable computational time for large-scale instances. Meanwhile, the comparison between the proposed heuristics and existing methods verifies the effectiveness and applicability of the proposed heuristics for solving MVRPCD and 2L-CVRP.

Trade-in price and base warranty length: A heuristic algorithm for concurrent optimization

The companies producing durable products consider various strategies to attract customers’ attention and maximize their profit. The two inseparable policies utilized by these companies are free-replacement warranty and Trade-in. Both policies profoundly impact customers’ buying behavior and producers’ profit. In this article, we investigate a monopolist manufacturer producing durable products over a horizon of two periods. This manufacturer simultaneously implements trade-in and base warranty policies for the products and proffers them to strategic customers. The goal is to find the optimal products’ prices, warranty length, and trade-in price, considering the influences of these parameters on the manufacturer’s profit in each period. According to the warranty length and the length of each period, we developed two mathematical models. In these models, the uniform and exponential distributions are deemed as the failure distributions of the products, and a heuristic solution algorithm is then proposed based on the problems' convexity. The numerical study showed that the optimal warranty length and trade-in price are contingent on production costs, failure distributions, innovation, and strategy level. In addition, it is concluded that a higher trade-in price should be offered if the manufacturer chooses without warranty policy.

Compressing and reconstructing the voltage data for lithium-ion batteries using model migration and un-equidistant sampling techniques

The long-term storage of the batteries' operating data is critical to tracing and analysing their historical use but challenged by the Trillions of bytes of raw data generated per day. For battery pack applications such as electrified transportation, recording the single-cell voltage requires tens of times more space than other signals such as the pack current. Therefore, an efficient data compressor for the voltage is urgently required to save storage. We here propose to record the entire current trajectory but only partial voltage data in the data-compressing phase to save space. Understanding that the battery's load profiles are often non-stationary, determining an optimum voltage-recording strategy is critical to the reconstruction accuracy but, unfortunately, an NP-hard problem. In this case, a heuristic method is proposed to seek a near-optimum solution with reduced computation. In addition, a battery model is also identified in the compressing phase so that the voltage trajectory can be readily calculated from the recorded current when data reconstructing is required. To compensate for the potential mismatch of the identified model, we establish a migration network using the recorded (partial) data. A piece-wise linear corrector is further fused into the reconstruction algorithm to not only guarantee zero errors at the voltage-recording points but also simplify the design of the above-mentioned heuristic optimisation algorithm. Experimental results show that the root-mean-squared-error of the reconstructed data could be bounded by 5 mV when more than 95% of the voltage data are compressed, paving the way to more efficient storage of large-scale battery operating data. • Lossy strategy is developed for battery pack data compressing and reconstructing. • Reconstructing error is bounded by 5 mV when the rate of compression equals 95%. • Un-equidistant recording is implemented by approximately solving an NP-hard problem. • A fused model migration strategy is developed for data reconstruction.

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

Comparison of Heuristic Approaches in Weight Optimization of Different Power Levels Transformers

ABSTRACT In this study, the weight of distribution type transformers and power transformers, which are used extensively in the industry, is optimized. A heuristic approach that uses the Firefly algorithm, Ant Colony algorithm, and the Bee algorithm to determine the weight of transformers is described. Optimization of the iron weight components effective in the weight of a transformer to be produced in multi-type is provided. It is aimed to reduce the transformer cost, increase the life cycle of the transformer, and increase the efficiency by obtaining the optimum current density (s) and iron section (C) values which are the variable parameters used in the iron weight calculation of the transformer. In the study, it is determined that heuristic optimization algorithms, which provide more effective and efficient values than traditional approaches, give better results. These popular algorithms, in the weight parameters of 50 and 1000 kVA transformer powers, can obtain a lower weight value of 9.6% with the weight obtained by the Bee algorithm, 7.4% by the Ant Colony algorithm and 11% by the firefly algorithm than the weight obtained through the classical method. According to the heuristic optimization results, it shown that optimum 50 and 1000 kVA transformer weights can be determined and less costly transformer designs can be made.

Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm.

In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications. In order to prevent possible pointing angle errors for asymmetric NUSLA, we employed a penalty function to ensure the peak at the desired direction. Modified reinforcement learning algorithm (MORELA), which is a reinforcement learning-based algorithm used to determine a global optimum of the cost function, is applied to optimize the spacing and weights of the NUSLA by minimizing the proposed cost function. The performance of the proposed scheme was verified by comparing it with that of existing heuristic optimization algorithms via computer simulations.

Support vector regression with heuristic optimization algorithms for predicting the ground surface displacement induced by EPB shield tunneling

Machine learning method with heuristic optimization algorithms is proposed to predict the stratum displacement induced by earth pressure balanced shield tunneling. Support vector regression is used as the machine learning method. Four heuristic intelligent optimization algorithms, namely, genetic algorithm, particle swarm optimization, grey wolf optimizer and sparrow search algorithm, are applied to optimize the two hyperparameters of support vector regression model, namely, penalty factor and bandwidth term. Simulated annealing algorithm is introduced to show the necessity of using heuristic algorithms. Mean square error of k-fold cross validation is considered as the fitness function for optimization algorithms. Normalization method and dummy variables are used for data preprocessing. For 115 samples from field measurement, 92 samples are used as the training set, and 23 samples are used as the test set. Three categories of parameters, namely, shield tunneling parameters, tunnel geometrical parameters and stratum types, are used as input parameters for the proposed method. Correlations among parameters are analyzed by Pearson correlation coefficient. The prediction results show that grey wolf optimizer and sparrow search algorithm are suitable methods for determining hyperparameters of support vector regression due to higher accuracy, efficiency, and stability.

Mine safety and risk prediction mechanism through nanocomposite and heuristic optimization algorithm

Worker's safety and water quality maintenance are the two predominant process that is followed inside the Mines. Many models had been proposed in the past to enhance the whole process but they failed to do so due to the practical limitations in the deployment in terms of cost. Though technology had improved much, human intervention is still unavoidable in mines. Considering the practical issues, in this article, a novel methodology using the Internet of Things (IoT) is proposed. This study is mainly focused on the change in the geometry/structure of the coal mining field due to pressure and water contamination that occurs in the region during coal extraction. Several sensors are adopted to measure the water quality and computational technique is used to analyze roof pressure variation in the coalfield. The roof pressure experienced by the nascent structure is compared with a structure that is roof bonded with a glass fiber composite layer. The significance of nanofiber technology is analyzed and reported. To ensure water quality, Harmonic Water Optimization (HWO) Algorithm is developed, here 10 different water flow areas are chosen and their corresponding data is gathered using IoT sensors. Harmony Management Preprocessing (HMP) Algorithm is developed to preprocess the redundant and ambiguous data. Heuristic Fusion Clustering (HFC)algorithm is developed for the data fusion, which can cluster and improve the data quality. The analysis of the proposed work reveals that it would greatly improve the sustainable development of mining safety and water quality which is the most vital factor.

A data-driven self-adaptive parameter tuning framework for composite automobile part optimization design

Population-based heuristic optimization algorithms are wildly used in the automobile optimization design. However, the hyper-parameter tuning has a significant effect on the performance of the most of the heuristic algorithms. In order to take full advantages of the heuristic optimization algorithms, this article proposes a data-driven framework for self-adaptive parameters tuning, which named DSPT. The DSPT framework divides the optimization process into two phases. In the learning phase, the knowledge is learned from abundant benchmark functions. The specifically designed performance metrics are used to relate the characteristics of different problems and algorithm performances. In the optimizing phase, the characteristics of a new problem are firstly extracted. According to the knowledge gained from the learning phase and the problem characteristics gained in this phase, rather than predetermined parameters based on experience, the key parameters are tuned automatically. Therefore, the optimization can continue more efficiently. Based on the newly proposed social spider inspired particle swarm optimization algorithm, the proposed framework is successfully applied to the multi-scale lightweight design of four different composite automobile parts.

ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands

This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast the electricity consumed and produced by renewable energy sources on an hourly level. Then, the forecasts are exploited by an algorithm that optimally allocates the operating hours of the pumps with the objective to minimize predicted peaks. Constraints related with the operation of the pumps are also considered. The performance of the proposed method is evaluated considering the case of a Greek remote island, Tilos. The island involves an energy management system that facilitates the monitoring and control of local water pumping stations that support residential water supply and irrigation. Results indicate that smart scheduling of water pumps in a small-scale island environment can reduce the daily and weekly deviation of electricity consumption by more than 15% at no monetary cost. It is also concluded that the potential gains of the proposed approach are strongly connected with the amount of load that can be shifted each day, the accuracy of the forecasts used, and the amount of electricity produced by renewable energy sources. • Electricity peak shaving in small scale islands. • Smart scheduling the operation of water pumping systems. • Exploiting ensembles of forecasting models for PV production and electricity consumption. • A heuristic optimization algorithm to support load shifting decisions.

A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization

Whale optimization algorithm (WOA) has received increasing attention in engineering optimization owing to its high computation efficiency, whereas, it has exhibited the drawback of premature convergence in solving multi-dimensional engineering global optimization problems. In this research, a niching hybrid heuristic whale optimization algorithm (NHWOA) is proposed to enhance convergence speed and search coverage in solving global optimization problems. In the algorithm, the niching technique is introduced to promote the diversity of population and restrain premature convergence in search of a global best solution. A heuristic adjustment to the parameters of the hybrid WOA algorithm is made to promote the exploration potential of search agents in the evolution. A designed perturbation to all the search agents’ positions is executed to avoid their falling into a local optimum. Optimization to the CEC2014 benchmark functions as validation cases are conducted along with comparisons to both conventional intelligent optimization algorithms and other state-of-the-art modified WOAs. Results indicate the effectiveness and superiority of NHWOA in solving the problems. Five practical engineering problems for global optimization with multiply variables are introduced to validate the performance of the presented algorithm with good performance results in the global computations.

Efficient Two-Dimensional Defect-Free Dual-Species Atom Arrays Rearrangement Algorithm with Near-Fewest Atom Moves

Dual-species single-atom array in optical tweezers has several advantages over the single-species atom array as a platform for quantum computing and quantum simulation. Thus, creating the defect-free dual-species single-atom array with atom numbers over hundreds is essential. As recent experiments demonstrated, one of the main difficulties lies in designing an efficient algorithm to rearrange the stochastically loaded dual-species atoms arrays into arbitrary demanded configurations. We propose a heuristic connectivity optimization algorithm to provide the near-fewest number of atom moves. Our algorithm introduces the concept of using articulation points in an undirected graph to optimize connectivity as a critical consideration for arranging the atom moving paths. Tested in array size of hundreds atoms and various configurations, our algorithm shows a high success rate (>97%), low extra atom moves ratio, good scalability, and flexibility. Furthermore, we propose a complementary step to solve the problem of atom loss during the rearrangement.