Heuristic Optimization Algorithm Research Articles

Multi-scale habitat selection modeling using combinatorial optimization of environmental covariates: A case study on nature reserve of red-crowned cranes

Multi-scale habitat selection modeling (HSM) has garnered attention due to its ability to incorporate scale dependence of species. The key of multi-scale HSM is to select the appropriate combination of scales for different resources or environmental conditions, and then construct a set of multi-scale environmental covariates as the features of HSM. However, the existing scale selection methods do not determine the combination of scales under a unified model. In this study, a combinatorial optimization approach is proposed. We regard the combination of different scales as a search space, and use a heuristic optimization algorithm to search for the best-fitting model to determine the optimal scales for each resource or environmental condition. In a case study conducted in Yancheng National Nature Reserve, the proposed approach is applied to model the habitat selection of the endangered red-crowned crane. We compare the proposed method with single-scale, random-scale and other multi-scale approaches. The results show that the combination of scales selected based on the proposed method obtained the best accuracy in the spatial prediction of habitat suitability with a test AUC of 0.865 for the daytime scenario and 0.932 for the nighttime scenario. Moreover, the selected scales are utilized to generate response curves, providing suggestions for habitat restoration and management of the red-crowned crane population in the nature reserve.

Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization.

Groundwater contaminant source identification (GCSI) has practical significance for groundwater remediation and liability. However, when applying the simulation-optimization method to precisely solve GCSI, the optimization model inevitably encounters the problems of high-dimensional unknown variables to identify, which might increase the nonlinearity. In particular, to solve such optimization models, the well-known heuristic optimization algorithms might fall into a local optimum, resulting in low accuracy of inverse results. For this reason, this paper proposes a novel optimization algorithm, namely, the flying foxes optimization (FFO) to solve the optimization model. We perform simultaneous identification of the release history of groundwater pollution sources and hydraulic conductivity and compare the results with those of the traditional genetic algorithm. In addition, to alleviate the massive computational load caused by the frequent invocation of the simulation model when solving the optimization model, we utilized the multilayer perception (MLP) to establish a surrogate model of the simulation model and compared it with the method of backpropagation algorithm (BP). The results show that the average relative error of the results of FFO is 2.12%, significantly outperforming the genetic algorithm (GA); the surrogate model of MLP can replace the simulation model for calculation with fitting accuracy of more than 0.999, which is better than the commonly used surrogate model of BP.

Review of application on improved ant colony algorithm

A heuristic global optimization algorithm is the Ant colony algorithm, with several advantages, such as robustness, so the algorithm can be used in many fields of our daily life. This article briefly explains some of the principles of the basic ant colony algorithm, and a detailed analysis of the representative improved algorithm models, is carried out. Moreover, the research status of Ant Colony Algorithm in several fields, like travelling salesman problem, path planning problem, routing problem are also summarized in this paper.

A review of reactive power optimization algorithm in power system

The paper analyses the significance of reactive power optimization, generalizes the current situation of power system development. Various optimization algorithms were introduced in this paper such as traditional optimization algorithm, intelligence optimization algorithm, including the methods of linear programming, Newtons method, heuristic optimization algorithm, etc. This research analyzes the advantages and disadvantages of each algorithm and its application direction by comparing their outstanding performance in solving discrete variables and continuous variables. The purpose of the research is to find the optimal solution of reactive power optimization algorithm, minimize the transport network loss of power system, and improve the quality of users.

Optimal tip shape for minimum drag and lift during horizontal penetration in granular media

AbstractHorizontal penetration in granular media is ubiquitous, from tunneling and geotechnical site investigation, to root growth and the locomotion of burrowing animals in nature. This contribution couples the discrete element method (DEM) with ant colony optimisation, a heuristic optimisation algorithm, to find the optimal tip shapes to minimise drag and lift forces during horizontal penetration. The tip which minimizes drag has a slender profile with a low tip curvature, to give a drag force that is $$15.6\%$$ 15.6 % lower compared to a conventional CPT intruder, however this shape induces a downwards force that increases with intruder depth. Conversely, the tip that minimizes lift is blunt, with a high tip curvature and short width, and reduces the drag and lift forces by $$4.5\%$$ 4.5 % and $$30.8\%$$ 30.8 % (respectively) compared to the CPT. The lift and drag forces are competing optimisation objectives, thus the tip shape with the optimal trade-off between drag and lift forces can be established using Pareto optimality. The Pareto optimal tip shape reduces the drag and lift forces by $$10.7\%$$ 10.7 % and $$19.4\%$$ 19.4 % respectively, and is strikingly similar to the profile of a sandfish. This contribution shows that when a common goal exists, bio-inspired solutions can offer an optimal solution to engineering applications. We also show the potential to integrate DEM simulations within an optimization framework to develop innovative design solutions.

A global-local hybrid strategy with adaptive space reduction search method for structural health monitoring

The difficulty in computational convergence poses challenges of application for traditional heuristic optimization algorithms to solve the optimization-based structural identification problem, especially for the large-scale and complex structural systems where considerable number of unknown parameters and degrees of freedom involved. Unlike the classic identification methods, in this paper, a novel hybrid strategy, coarsely exploring the relatively large search limits with the improved Jaya algorithm and adaptive search space reduction method in the global stage, and then fine-tuning the identified best solution with local optimization methods to the optimum in the local stage, is proposed and evaluated. The improved Jaya algorithm includes three improvements compared to its original version, fuzzy clustering competitive learning, experience learning and Cauchy mutation mechanisms. Gradient based Levenberg-Marquardt method, sequential quadratic programming method and non-gradient based Nelder-Mead simplex method are inserted as local mathematical optimizers to further enhance identification accuracy and efficiency. The superiority of proposed improved Jaya algorithm is validated in optimizing classical and CEC05 benchmark functions by comparing with several state-of-the-art algorithms. Furthermore, the effectiveness of proposed global-local hybrid method is verified by a numerical example of truss structure and an experimental test of the steel grid benchmark structure with incomplete set of noise-polluted measurements. The statistical results show that the improved Jaya algorithm and adaptive search space reduction method combined with sequential quadratic programming can achieve better performance in structural damage identification than other methods.

Meta Heuristic Optimization Algorithm based Rectifier Control in Switched Reluctance Generator

A simple, robust, economical and highly efficient 8/6 switched reluctance (SR) generator is connected to a wind turbine to increase the amount of power extracted from the wind. Output power of the generator is fed to the PWM rectifier via a four-phase asymmetric half-bridge (AHB) converter and maximum power point tracking (MPPT) of the rectifier is assisted with torque ripple minimization to present an effective control as it allows torque ripple suppression arising from the SR generator nature while capturing maximum wind power. The wide range of speed variation of the SR generator is minimized using the combination of PI and hysteresis current controller. Further, the output of the AHB converter is directed to the conventional PWM rectifier. The purpose of this research is to deliver ripple-free dc power to a dc grid/load or battery. Hence, in this paper, the GWO-based optimized PI controller is implemented as an MPPT method to maintain the constant dc bus voltage. The inherent ability of GWO optimization-based control of the rectifier system ensures better dynamic control. The simulation is performed in a MATLAB environment for a power rating of 1000W standalone system and tested using an FPGA controller. This paper carries out a stability analysis of the system by varying the wind velocity.

Workflow aware analytical model to predict performance and cost of serverless execution

SummaryServerless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively.

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

As the wind speed is intermittent and unpredictable, statistical distribution approaches have been used to describe wind dates. The Weibull distribution with two parameters is thought to be the most accurate way for modeling wind data. This study seeks wind energy assessment via searching for optimal parameter estimation of the Weibull distribution. For this target, several analytical and heuristic methods are investigated. The analytical methods such as maximum likelihood method, moment method, energy pattern factor method (EPFM), and empirical method (EM) are used to find these optimal parameters. Also, these parameters are obtained by four heuristic optimization algorithms called particle swarm, crow search, aquila optimizer, and bald eagle search optimizers. The simulation results of analytical and heuristics are assessed together to identify the best probability density function (PDF) of wind data. In addition, these competitive models are submitted to find the most appropriate model to represent wind energy production. In all methods, the error between actual and estimated wind energy density is computed as the target fitness function. The simulation tests are carried out based on per year real data that are collected from Zafaranah and Shark El-Ouinate sites in Egypt. Also, different indicators of fitness properties are assessed such as the root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and wind production deviation (WPD). The simulation results declare that the proposed bald eagle search optimization algorithm offers greater accuracy than other analytical and heuristic algorithms in estimating the Weibull parameters. Besides, statistical analysis of the compared methods demonstrates the high stability of the BES algorithm. Moreover, the BES algorithm presents the fastest convergence compared to the others. Furthermore, different models are analyzed to deduce the nonlinear relationship between the wind output power and the regarding speed where the error of wind energy density between actual and estimated is greatly minimized using the cubic model at least values of statistical indicators.

An improved heuristic mechanism ant colony optimization algorithm for solving path planning

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.

A RUL prediction method for lithium-ion batteries based on improved singular spectrum analysis and CSA-KELM

Accurate and efficient capacity prediction of lithium-ion batteries plays an important role in improving performance and ensuring safe operation. An improved singular spectral analysis (ISSA) based on the fractal dimension (FD) is proposed to eliminate capacity regeneration during battery degradation. Firstly, FD is used to determine whether the signal decomposition component belongs to the main component or the noise is a useless component. Secondly, the kernel extreme learning machine (KELM) is introduced to predict the main components. In addition, a new heuristic optimization algorithm, the circle search algorithm (CSA), is used to optimize the regularization coefficients and kernel function parameters of KELM. Finally, experimental simulations are performed based on four batteries from two open lithium-ion battery datasets. The results show that the ISSA algorithm has better decomposition efficiency than the empirical mode decomposition (EMD) and the variational mode decomposition (VMD). Compared with other intelligent optimization algorithms, the CSA is superior and can achieve a faster convergence rate.

A Memory-Constraint-Aware List Scheduling Algorithm for Memory-Constraint Heterogeneous Muti-Processor System

An effective scheduling algorithm is vital for the execution efficiency of applications on Heterogeneous Muti-Processor System (HMPS), especially Memory-Constraint Heterogeneous Muti-Processor System (MCHMPS). Stringent local and external memory constraints have significant impact on the execution performance of applications executed on MCHMPS, predictability is also a critical factor for task scheduling on MCHMPS. Therefore, a novel list scheduling algorithm termed Memory-constraint-aware Improved Predict Priority and Optimistic Processor Selection Scheduling (MIPPOSS), essentially a heuristic search optimization algorithm, is proposed in this paper. In MIPPOSS, a predictive approach is applied for task prioritization and processor selection, and a novel memory-constraint-aware approach is employed in the processor selection phase. MIPPOSS has polynomial complexity and produces better results for application scheduling on target architecture. Randomly generated DAGs and 3 real-world applications experiments, including Cybershake, LIGO, and Montage, show that MIPPOSS outperforms the other five competing algorithms by a large margin.

Optimizing Emergency Supply Location Selection in Urban Areas: A Multi-Objective Planning Model and Algorithm

The scientific location and layout of emergency material storage and rescue points in urban areas are critical aspects of emergency management. In this study, a multi-objective programming optimization model was constructed based on related theories, incorporating multiple goal combinations with different dimensions according to various disaster scenarios and urban emergency needs. The weight factors of emergency timeliness, economy, and safety were considered, and the multi-objective model optimization problem was transformed into a single-objective comprehensive optimization model problem using the weight method. The analysis decision function was utilized to study the transformation and solution method of the urban emergency rescue point location model. Heuristic optimization algorithms were employed to perform average segmentation calculations on the preset neighborhoods, constantly changing and narrowing the neighborhood range until the algorithm termination conditions were met, approaching the domain range of the optimal solution. Additionally, another precision parameter was utilized to control the accuracy of the final solution neighborhood range. The optimization of emergency vehicle scheduling was used to synergistically solve the problem of reserve rescue point location layout and optimization solution. The results of the example demonstrate the feasibility of constructing a multi-objective model with multiple combinations of different dimensions of objectives and the rationality of the Dijkstra heuristic optimization algorithm used. This study provides multiple methodologies and alternative site selection plans for decision-makers to select the required multi-objective reserve rescue point location model based on different urban disaster situations and their own emergency rescue needs.

Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling

Ensemble approaches can be a quick way to improve hydrological prediction accuracy by considering multiple plausible modeling representations of a system. Previous studies showed that the use of multiple types of models could increase prediction accuracy, but model selection subjectivity and parameter equifinality issues remain unsolved. This study proposes to consider multiple parameter sets identified in the model calibration process and information on their performance for improved accuracy of ensemble hydrological prediction. In this study, we investigate whether multi-parameter ensemble (MP) compared to multi-model ensemble (MM) approaches can improve the accuracy of hydrology and water quality prediction. A variety of ensemble methods, including simple averaging, median filtering, weighted averaging, Bayesian model averaging (BMA), and their variants, were tested in watershed modeling implemented to predict daily water discharge and total phosphorus (TP) loads of a study watershed. The parameter spaces of two watershed loading models, Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-FORTRAN (HSPF), were sampled using a multi-objective heuristic optimization algorithm, namely A Multi-Algorithm Genetically Adaptive Multiobjective (AMALGAM). The modeling experiment showed that the MP approach more effectively improved modeling accuracy statistics compared to the MM of both models as well as single-model and single-parameter approaches. The ensemble approaches did not always improve modeling accuracy, and their efficiency depended on ways to determine weights for ensemble members. The BMA method outperformed the other weighting schemes tested in this study, and its performance was responsive to the number and diversity (or variance) of ensemble member candidates and the computational cost to create the candidates. The findings demonstrate how the information of parameter sets sampled in model calibration and their performance statistics could help improve hydrological prediction accuracy.

Improved Bayesian Optimization Framework for Inverse Thermal Conductivity Based on Transient Plane Source Method

In order to reduce the errors caused by the idealization of the conventional analytical model in the transient planar source (TPS) method, a finite element model that more closely represents the actual heat transfer process was constructed. The average error of the established model was controlled at below 1%, which was a significantly better result than for the analytical model, which had an average error of about 5%. Based on probabilistic optimization and heuristic optimization algorithms, an optimization model of the inverse heat transfer problem with partial thermal conductivity differential equation constraints was constructed. A Bayesian optimization algorithm with an adaptive initial population (BOAAIP) was proposed by analyzing the influencing factors of the Bayesian optimization algorithm upon inversion. The improved Bayesian optimization algorithm is not affected by the range and individuals of the initial population, and thus has better adaptability and stability. To further verify its superiority, the Bayesian optimization algorithm was compared with the genetic algorithm. The results show that the inversion accuracy of the two algorithms is around 3% when the thermal conductivity of the material is below 100 Wm-1K-1, and the calculation speed of the improved Bayesian optimization algorithm is three to four times faster than that of the genetic algorithm.

A multi-population particle swarm optimization algorithm with adaptive patterns of movement for the stochastic reconstruction of petroleum fractions

Molecular reconstruction (MR) methods are used to estimate the detailed composition of a hydrocarbon mixture at the molecular level based on experimental data. Stochastic reconstruction (SR) is one of the MR approaches available to this end. Obtaining the solution involves estimating a set of probability density function (PDF) parameters with a global optimization algorithm. However, to the author's knowledge, no other study has thoroughly explored using metaheuristics to improve parameter estimation in this problem. By combining ideas from Chang (2015) and Bonyadi (2018), this study proposes a novel heuristic optimization algorithm named Multi-Population Particle Swarm Optimization with Adaptive Movement Patterns (MPSO-AMP). Since multiple swarms are employed simultaneously with unique movement patterns, the proposed algorithm can render more exact solutions in SR problems than conventional particle swarm optimization (PSO) and other extensions from the literature.

Mutation based improved dragonfly optimization algorithm for a neuro-fuzzy system in short term wind speed forecasting

The Dragonfly algorithm (DA) is a heuristic optimization algorithm that is commonly used for complex optimization problems. Despite its widespread application, the abundance of social behaviors in its construct can lead to poor accuracy in solutions and an imbalance between exploration and exploitation phases. To overcome these issues, this paper proposes a mutation-based Dragonfly optimization algorithm (MIDA). In order to increase the solution accuracy of the original DA and reduce its handicaps, the proposed model includes three procedures, namely, mutation operation, boundary control, and greedy selection mechanisms. The mutation operator helps to find global optima by avoiding getting stuck at the local optimum point, while the boundary control and greedy selection mechanisms update the dragonflies at each iteration and thus use the better fitness value of the updated ones. The performance of the proposed MIDA is tested and shown to be superior to the original DA using several analyses such as convergence, search history, trajectory, average distance, computational complexity, diversity and balance. To validate the MIDA algorithm, it is tested on the 10, 30, 50 and 100 dimensions of the CEC2014 benchmark functions. Furthermore, a comparison against twelve state-of-the-art (SOTA) meta-heuristic optimization algorithms is performed. The performance of the proposed MIDA is also compared with the performances of some improved dragonfly algorithms taken from the literature. The statistical results obtained by the original DA and MIDA for ten minimization problems with 5, 10, 15 and 20 dimensions from the CEC2020 test suite are presented. Finally, the proposed algorithm is applied to optimize the ANFIS model parameters in order to be used for short-term wind forecasting as a real-world problem. The results obtained for the different dimensions of CEC2014 and CEC2020 test problems in the study show that the search performance of proposed MIDA is better than that of the original DA. MIDA outperformed the original DA by 91.33% for CEC2014 benchmarks and 94.25% for CEC2020 benchmarks. In addition, MIDA came first in comparison with twelve different SOTAs from the literature. In comparisons with different DA versions, MIDA took the second place in terms of statistical performance. Computational complexity was examined in the CEC2020 benchmarks and it was seen that MIDA has a more effective run time than DA. Finally, the short-term wind speed forecasting results of the ANFIS-DA hybrid model according to four different error metrics are more successful than those of the ANFIS-DA hybrid model.

A Method Based on Timing Weight Priority and Distance Optimization for Quantum Circuit Transformation

In order to implement a quantum circuit on an NISQ device, it must be transformed into a functionally equivalent circuit that satisfies the device's connectivity constraints. However, NISQ devices are inherently noisy, and minimizing the number of SWAP gates added to the circuit is crucial for reducing computation errors. To achieve this, we propose a subgraph isomorphism algorithm based on the timing weight priority of quantum gates, which provides a better initial mapping for a specific two-dimensional quantum architecture. Additionally, we introduce a heuristic swap sequence selection optimization algorithm that uses a distance optimization measurement function to select the ideal sequence and reduce the number of SWAP gates, thereby optimizing the circuit transformation. Our experiments demonstrate that our proposed algorithm is effective for most benchmark quantum circuits, with a maximum optimization rate of up to 43.51% and an average optimization rate of 13.51%, outperforming existing related methods.

Crossover Rate Sorting in Adaptive Differential Evolution

Differential evolution (DE) is a popular and efficient heuristic numerical optimization algorithm that has found many applications in various fields. One of the main disadvantages of DE is its sensitivity to parameter values. In this study, we investigate the effect of the previously proposed crossover rate sorting mechanism on modern versions of DE. The sorting of the crossover rates, generated by a parameter adaptation mechanism prior to applying them in the crossover operation, enables the algorithm to make smaller changes to better individuals, and larger changes to worse ones, resulting in better exploration and exploitation. The experiments in this study were performed on several modern algorithms, namely L-SHADE-RSP, NL-SHADE-RSP, NL-SHADE-LBC and L-NTADE and two benchmark suites of test problems, CEC 2017 and CEC 2022. It is shown that crossover rate sorting does not result in significant additional computational efforts, but may improve results in certain scenarios, especially for high-dimensional problems.

A reinforcement learning approach to Automatic Voltage Regulator system

An Automatic Voltage Regulator (AVR) system utilized to keep the terminal voltage of a synchronous generator at the desired level has received much attention among researchers. Designing an efficient and robust control scheme for the AVR system to maintain a specified voltage level is an important research area. From the control area perspective, reinforcement learning, an adaptive optimal control method, has received increasing attention in reference tracking problems. This article discusses a reinforcement learning approach to an AVR system and its experimental validation. A deep deterministic policy gradient (DDPG) agent working in continuous-time is designed offline to improve dynamic system characteristics of the AVR system besides its robustness against load disturbance, parameter uncertainties, and reference change. In the DDPG agent design process, the limits of the produced control signal are taken into account to perform a feasible simulation similar to a real-time application. The performance of the proposed learning-based controller is analyzed in three categories: transient and steady-state responses, stability analysis, and robustness analysis against parameter uncertainties, reference change, and load disturbance. A comparison with recently published papers employing Fuzzy-PID, PID-F, PIλDND2N2, PIDD2 ,and PID controllers in which various heuristic optimization algorithms were employed to optimally tune the controller parameters is made. Furthermore, to demonstrate that the behavior of the learning-based approach provides a stable and satisfactory performance, it is analyzed for a real synchronous generator connected to a 230 kV network using Matlab/Simulink environment. The results presented in this paper indicate that the proposed learning-based controller ensures the stability of the AVR system, significantly improves the regulating performance, and most impressively, is robust against parameter uncertainties, reference change, and load disturbance.