Hooke Jeeves Research Articles

Математическое обеспечение информационно-управляющей системы для хранения продукции с ограниченным сроком хранения

The work presents a solution to the problem of developing mathematical support for an information and control system for storing products with a limited shelf life using the example of storing sugar beets in field conditions and in heaps at sugar factories based on experimental experiments published in the open press data. An end-to-end ranking of methods for storing root crops was carried out, starting with a root lying separately on the field (rank 0.1 points), from simple windrows and heaps (rank 0.2 points) to specialized root storage and ventilated piles covered with special bactericidal coatings with reflective and protective properties (rank 4 points). An end-to-end ranking of mechanical damage to sugar beet roots has been implemented, starting from 1 point (the root crop is practically undamaged) to 0.1 point (fragments and tails). The studied hybrids were ranked according to sugar content. Based on the rankings, taking into ac-count storage time and temperature, mathematical models (mm) of the dependence of technological indica-tors of beet raw materials, including losses of beet mass and sucrose, on storage parameters were built for each study. Extensive computational experiments were per-formed using an interactive identification and optimization system (isio), which is based on a genetic algorithm and the Hooke Jeeves nonlinear programming method. The errors of the developed models do not exceed the errors of the experimental data used to construct them.

Optimization of solar and heat pump complementary powered desiccant air conditioning system

The effective utilization of renewable heat sources is critical for energy saving in desiccant air conditioning systems. A new solar and heat pump complementary powered desiccant air conditioning system is proposed. The system consists of a solar and water source heat pump complementary reheat subsystem, a two-stage desiccant wheel, a three-stage air cooler, and a two-stage heat pipe exchanger. Simulation are performed in the transient simulation software TRNSYS, and the particle swarm optimization and the Hooke Jeeves algorithm with life cycle cost as the objective function are applied to optimize the collector area, the heat storage tank volume and the water-source heat pump power. The results show that the monthly average coefficient of performance of the system is 5.1, 5.6, 5.5 and 4.8 in June, July, August and September, respectively. Compared to the conventional air conditioning system, the new system has a 58.4% reduction in operation cost and a payback period of 6.6 years. The CO2 emission reduction rate of the new system can reach 65.2% in July and 65.1% in August, while it is the lowest in September at 62.9%. Based on the above, the new system has better energy efficiency, economic and environmental benefits.

Application of Deep Reinforcement Learning for Proportional–Integral–Derivative Controller Tuning on Air Handling Unit System in Existing Commercial Building

An effective control of air handling unit (AHU) systems is crucial not only for managing the energy consumption of buildings but ensuring indoor thermal comfort for occupants. Although the initial control schema of AHU is appropriate at installation and testing, it is frequently necessary to adjust the control variables due to the changing thermal response of the building envelope and space usage. This paper presents a novel optimization process for the control parameters of old AHU systems in existing commercial buildings without system downtime and massive operational data. First, calibrating the building and system simulator with limited system operation data and unknown building parameters can provide identical responses to the system operation with the Hooke–Jeeves algorithm during the cooling season. The deep deterministic policy gradient algorithm is employed to determine the optimal control parameters for the valve opening position of the cooling coil within less than three hours of training based on the calibrated simulator. By using actual implementations with the developed optimal control variables for an old AHU in a real building, the proposed auto-tuned PID control in the simulator and with machine learning improves thermal environments with a steady room temperature (23.5 ± 0.5 °C) by 97% in occupied periods. It is also proved that this can reduce cooling energy consumption by up to 13.71% on a daily average. The successful AHU controller can improve not only the stability of AHU systems but the efficiency of a building’s energy use and indoor thermal comfort.

A MEMETIC ALGORITHM APPLIED TO INDUCTION MACHINE PARAMETERS IDENTIFICATION BASED ON AN OUTPUT ERROR

Based on an output error, several evolutionary methods have been applied to identify the parameters of an Induction Machine (IM). The main drawback of these methods is their premature convergence in many situations. To overcome this issue and achieve a more accurate solution, this paper proposes a Memetic Algorithm (MA), which combines a Genetic Algorithm (GA) and a local search method. This approach uses the Hooke-Jeeves (HJ) method for the local search as a mutation operator.GA has proven good ability in global search. The HJ method has a good ability to refine the local search and achieve the optimal accuracy solution. The proposed MA, which maintains a tradeoff between exploration and exploitation strategies, is applied to minimize the related objective function to obtain the electrical and mechanical machine parameters. The validation of the method is confirmed by an experiment carried out on an (0.4 kW) IM with parameters estimated using the measured data. Using the estimated parameters, the computed transient and steady-state currents agree well with the measured data.

Bi-level programming and multi-objective optimization for the distribution of resources in hierarchical organizations

The decision regarding the hierarchical distribution of resources is crucial for many organizations that want to allocate such resources efficiently. At the upper level of the hierarchy, a decision-maker may have an objective and a set of feasible solutions partially determined by the lower level. Nevertheless, the upper level can influence but not control the decision-maker at the lower level. Moreover, the objectives of the upper and lower levels are conflicting. Hence, the hierarchical distribution of resources can be formulated as a bi-level programming model. This paper reformulates the hierarchical allocation of resources. The Hooke-Jeeves (HJ) algorithm and the hybrid scatter search–Nelder-Mead (SSNM) method are proposed to solve the newly formulated problem. The problem is also analyzed from a multi-objective perspective to equally prioritize the upper and lower levels while subjecting each to an interdependent set of constraints; a multi-objective scatter search algorithm (MSSA) was developed for that purpose. Tailor-made instances were also designed for the implementation of the developed algorithms. The findings demonstrated that the HJ algorithm provides the best solution according to the upper-level objective function. However, it does not guarantee the best result for the lower level. In comparison, MSSA has a set of best possible solutions for both objectives. The hybrid SSNM method could not match the results provided by the former methods.

Micro-damage analysis and numerical simulation of composite solid propellant based on in situ tensile test

Abstract In order to quantitatively analyze the mesoscopic damage process of hydroxyl-terminated polybutadiene composite solid propellant under external load, periodic boundary conditions were applied to the representative volume element model based on sample composition and morphology, the mixed matrix containing aluminum powder was homogenized, and the hyperelastic matrix damage and bilinear/exponential particle–matrix interface cohesive model with initial damage were compiled through the secondary development of Abaqus. At the same time, a data interaction platform was constructed by means of Python and MATLAB, matrix and cohesion parameters were inverted according to the optimization algorithm and experimental data, and the whole process of propellant damage and fracture was simulated from the mesoscopic perspective. The results show that combining the adaptive particle swarm optimization algorithm and the Hooke–Jeeves algorithm can achieve the global optimal parameter inversion in 102 calculations, compared with the single local search algorithm, which can cut about 11% of the objective function values. Considering the matrix damage and the exponential cohesion model with initial damage, the optimal objective function value is 0.01635, which can more accurately simulate the propellant damage and fracture process compared with 0.02136 of a bilinear cohesion model.

Optimal allocation method of multi-energy system based on hybrid optimization algorithm

With the rapid development of industry, the research of energy storage technology and renewable energy continues to be hot, and the energy industry opens the era of diversification. Multi-energy complementary has become a new trend in sustainable energy development, leading the energy industry to a new energy system of deep integration and integration of multiple energy sources. This paper proposes a hybrid optimization algorithm that combines particle swarm algorithms and Hooke–Jeeves​ (HJ) with a comprehensive evaluation index as the optimization objective, aiming to improve the speed of solving the capacity optimization of integrated energy systems. The multi-energy system configuration optimization platform that covers the index system, optimization model, and system analysis module was established to systematically solve the integrated energy system optimization configuration problem, moreover provide an important reference for integrated energy system design and implementation. Besides, the influence of optimization algorithms on the configuration results was analyzed. Taking the combination of soil source heat pump system and combined cooling, heating and power system as an example, this study quantifies and compares the optimization results and solution speeds of the hybrid algorithm and the traditional single optimization calculation. It is shown that the hybrid optimization algorithm reduces the amount of iteration steps by approximately 31% compared with the particle swarm algorithm and by approximately 48% compared with the HJ algorithm. This significantly improves the speed of the optimization computation while ensuring the accuracy of the computation results.

A Modified Hooke–Jeeves Algorithm in Two-Player Nonlinear Static Game: With an Application in Forest Management

Strategy considerations for optimal outcomes play the key roles in every decision-making problems. Game theory in itself preserves the sustainability to draw some decision-making conclusions. Different forms of game exist in literature. This article is concerned with two-player nonlinear static game. We consider here the solution of the game problems through the algorithmic structures, with some modification, of Hooke–Jeeves (HJ) algorithm which is a tool to encounter unconstrained optimization. Basically, we lay emphasis on the modification of the considered function which is to be optimized. Hence, the name of the proposed algorithm is modified HJ algorithm. Numerous articles present different solutions of various real-life problems using HJ algorithm. Forest management problem is considered here under modified HJ algorithm to get solution. This article concludes with some comparative analysis with respect to some different methods, for example, analytical method and graphical method. Our proposed methodology gives better results than the results obtained in analytical method and graphical method with respect to optimal strategy sets and optimal payoff values.

Cross-Sectional Shape Optimization of Cylindrical Elastomer Spring for Sensitive Cargo Container

High-value sensitive cargoes are often damaged by low-frequency vibration and shock of containers during land and rail transport processes or mixed transport processes. Therefore, a dedicated cylindrical elastomer spring that absorbs vibration transmitted into the container has been developed. This study developed an optimal shape using a polyurethane material instead of the existing rubber spring. Elastomer spring requires an optimal design that satisfies the design target stiffness and strength by nonlinear finite element analysis. In order to develop an elastomer spring for a cargo container, the material constant was obtained by a hyperelastic behavior test of natural rubber, and based on this, the necessary optimal material constant of the new spring was predicted. In addition, nonlinear structural analysis was performed using ABAQUS to obtain the optimal shape of the spring, and optimal design was performed with I-SIGHT software. As a result of the sum of squared difference minimization with the comparison algorithm, it was found that the polyurethane material constant C10, C20, and C30 with the same characteristics as natural rubber was obtained. In addition, analysis using three optimization algorithms, Hooke–Jeeves algorithm, multi-island genetic algorithm, and optimal Latin hypercube, yielded a maximum principal strain of 0.244 of the spring obtained through the optimal cross-sectional shape design. It was found that this value was about 39% lower than the natural rubber spring in use. As a result of the compression load-displacement test of the actually developed product, it was confirmed that the correlation coefficient between the predicted value and the measured value was 0.928.

Aircraft parameter estimation using a novel hybrid Luus–Jaakola/Hooke–Jeeves neural-network based optimization technique

The neural network–based aircraft parameter estimation techniques have gained prominence in the last decade. Neuro–Gauss–Newton (NGN) technique is a widely used neural network–based algorithm. Although the NGN technique is useful for aircraft parameter estimation, it has some limitations. The present study is motivated by the limitations of the NGN method in estimating aircraft parameters, and hence, a new hybrid Luus–Jaakola/Hooke–Jeeves (LJ/HJ) method is proposed. The hybrid LJ/HJ method is based on two direct search methods: the Luus–Jaakola (LJ) method and the Hooke–Jeeves (HJ) method. In the hybrid LJ/HJ method, a global search of the minima of the error cost function is performed using the Luus–Jaakola method in the first phase. After that, the final solution is obtained using the Hooke–Jeeves method. The parameters estimated by the hybrid LJ/HJ method are compared with those estimated using the NGN method. The results obtained from the hybrid method are accurate, and unlike gradient-based optimization methods, the convergence issues are not observed during the estimation of parameters.

Identification of arch dam mechanical parameters based on sensitivity analysis and Hooke–Jeeves algorithm optimization

To address the problem in which the mechanical parameters of a dam’s body and foundation are affected by a complex environment and changes during the service period, this study proposes an arch dam mechanical parameter inversion technique based on the sensitivity analysis of the Morris method and the Hooke–Jeeves algorithm. First, a Kriging mathematical model that reflects the nonlinear mapping relationship between the radial displacement of an arch dam and the mechanical parameters of an arch dam’s body and foundation is established. The sensitivity of the mechanical parameters of an arch dam and its foundation to the radial displacement of an arch dam is analyzed on the basis of the Morris method, and the mechanical parameters to be inverted are determined. Second, an optimization mathematical model based on the Kriging mathematical model is proposed to calculate the mechanical parameters of a dam’s body and foundation and the inversion of the displacement of an arch dam. The inversion problem of the mechanical parameters of a dam’s body and foundation is transformed into an optimization problem of the objective function. Finally, the Hooke–Jeeves algorithm is used to optimize the objective function, and the mechanical parameters of a dam’s body and foundation are inversely obtained. The case study shows that the relative errors of the mechanical parameters of the arch dam retrieved using the proposed method are all within 10%. Results indicate that the proposed parameter identification method is reasonable and effective. It provides a new idea for the parameter inversion of other hydraulic structures.

Combination of quadratic ranking selection real-coded genetic algorithm with the Hooke-Jeeves optimization method for solar photovoltaic parameter estimation

Estimating accurate parameters for Photovoltaic cell (PVC) models plays a very important role in evaluation, simulation, control and maximum power point tracking (MPPT) of solar PV systems. Indeed, parameter extraction for solar cells and modules from experimental data is a challenging problem. These parameters must be evaluated from a set of nonlinear equations that cannot be solved analytically. Therefore, an optimization approach is essential to estimate these parameters by minimizing the difference between the calculated current from the PV model and the measured current. In this paper, a real-coded genetic algorithm (GA) based on quadratic ranking selection combined with the Hooke-Jeeves (HJ) method is proposed for this parameter extraction. It is well known that GA has proven good capacities in global search while on the other hand, the HJ has high-performance capacities in local search. As a hybrid technique, the HGAHJ has the feature of combining the capabilities of global and local searching. The proposed algorithm is developed and then applied for the parameter estimation for the single and double diode solar cell models. In addition, a comparative study with the GA and particle swarm optimization (PSO) algorithms is presented to demonstrate the effectiveness of the proposed approach using measured data of two commercial solar panels, which are Kyocera KC200GT and RTC France. Results reveal the superior capabilities of the suggested HGAHJ algorithm as a global optimization technique.

Structural damage identification considering uncertainties based on a Jaya algorithm with a local pattern search strategy and L0.5 sparse regularization

This study develops an improved Jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the Jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified Jaya algorithm is developed by integrating the one-step K-means clustering, Hooke–Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard Jaya algorithm, which is verified by a series of high-dimension test functions. An L0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties.

Investigation of a solar heating system assisted by coupling with electromagnetic heating unit and phase change energy storage tank: Towards sustainable rural buildings in northern China

• New coupled solar heating system proposed for clean heating of rural buildings. • Hooke–Jeeves algorithm is adopted to optimise four key parameters in a SHS. • LCC reduced by 4.17%–23.62%; CO 2 emission reduced by 13.07–26.73 T. • Based on applicability evaluation, the cost of investment grant is more operative than operating expense grants. Detached buildings in rural areas have considerable potential to promoting the application of solar heating systems (SHSs) from the perspective of low-carbon development. However, SHSs are designed to operate at the maximum building load, leading to energy wastage. To optimally design the key parameters of a SHS assisted by coupling with an electromagnetic heating unit and a phase change energy storage tank (SAEPT), a simulation model was established through the dynamic cosimulation of Designer's Simulation Toolkit and Transient System Simulation Program between the hourly heating supply and the hourly load of the building. The area of solar collectors, collector inclination, tank volume, and electromagnetic energy heating unit power were selected as the optimisation parameters, with life cycle cost (LCC) as the objective function. Generic Optimization Program was used to adopt the Hooke–Jeeves algorithm to iteratively optimise the configuration. Compared with the designed values, LCC was reduced by 4.17%–23.62%, and the CO 2 emission reduction was 13073–39801 kg. To evaluate the applicability of SAEPT in 16 regions, a multiattribute evaluation of energy-economic-environment was conducted based on 11 specific indicators. SAEPT was particularly suitable for regions with abundant solar energy resources and relatively low load of buildings. The payback period was approximately 3.78–7.98 years. The initial investment subsidy by the government can be between 0% and 25%. The findings of this study provide crucial insights for policymakers and SHSs promotion, which can facilitate sustainable low-carbon rural buildings in northern China.

Computational Methods for Parameter Identification in 2D Fractional System with Riemann-Liouville Derivative.

In recent times, many different types of systems have been based on fractional derivatives. Thanks to this type of derivatives, it is possible to model certain phenomena in a more precise and desirable way. This article presents a system consisting of a two-dimensional fractional differential equation with the Riemann–Liouville derivative with a numerical algorithm for its solution. The presented algorithm uses the alternating direction implicit method (ADIM). Further, the algorithm for solving the inverse problem consisting of the determination of unknown parameters of the model is also described. For this purpose, the objective function was minimized using the ant algorithm and the Hooke–Jeeves method. Inverse problems with fractional derivatives are important in many engineering applications, such as modeling the phenomenon of anomalous diffusion, designing electrical circuits with a supercapacitor, and application of fractional-order control theory. This paper presents a numerical example illustrating the effectiveness and accuracy of the described methods. The introduction of the example made possible a comparison of the methods of searching for the minimum of the objective function. The presented algorithms can be used as a tool for parameter training in artificial neural networks.

A hybrid of Bayesian-based global search with Hooke–Jeeves local refinement for multi-objective optimization problems

The proposed multi-objective optimization algorithm hybridizes random global search with a local refinement algorithm. The global search algorithm mimics the Bayesian multi-objective optimization algorithm. The site of current computation of the objective functions by the proposed algorithm is selected by randomized simulation of the bi-objective selection by the Bayesian-based algorithm. The advantage of the new algorithm is that it avoids the inner complexity of Bayesian algorithms. A version of the Hooke–Jeeves algorithm is adapted for the local refinement of the approximation of the Pareto front. The developed hybrid algorithm is tested under conditions previously applied to test other Bayesian algorithms so that performance could be compared. Other experiments were performed to assess the efficiency of the proposed algorithm under conditions where the previous versions of Bayesian algorithms were not appropriate because of the number of objectives and/or dimensionality of the decision space.

A New Sampling Mismatch Compensation Method for Moving Target Detection Based on Hooke–Jeeves Optimization Processing

A New Sampling Mismatch Compensation Method for Moving Target Detection Based on Hooke–Jeeves Optimization Processing

Numerical Simulation and Parameter Analysis of Electromagnetic Riveting Process for Ti-6Al-4V Titanium Rivet

Electromagnetic riveting process (EMR) is a high-speed impact connection technology with the advantages of fast loading speed, large impact force and stable rivet deformation. In this work, the axisymmetric sequential and loose electromagnetic-structural coupling simulation models were conducted to perform the electromagnetic riveting process of a Ti-6Al-4V titanium rivet, and the parameter analysis of the riveting setup was performed based on the sequential coupled simulation results. In addition, the single-objective optimization problem of punch displacement was conducted using the Hooke–Jeeves algorithm. Based on the adaptive remeshing technology adopted in air meshes, the deformation calculated in the structural field was well transferred to the electromagnetic field in the sequential coupled model. Thus, the sequential coupling simulation results presented higher accuracy on the punch speed and rivet deformation than the loose coupling numerical model. The maximum relative difference of electromagnetic force (EMF) on driver plate and radial displacement in the rivet shaft was 34.86% and 13.43%, respectively. The parameter analysis results showed that the outer diameter and the height of the driver plate had a significant first-order effect on the response of displacement, while the platform height, transition zone height, angle, and transition zone width of the amplifier presented a strong interaction effect. Using the obtained results on the optimal structural parameters, the punch speed was effectively improved from 6.13 to 8.12 m/s with a 32.46% increase. Furthermore, the displacement of the punch increasing from 3.38 to 3.81 mm would lead to an 80.55% increase in the maximum radial displacement of the rivet shaft. This indicated that the deformation of the rivet was efficiently improved by using the optimal rivet model.

Multiple global optima location using differential evolution, clustering, and local search

Multimodal optimization can be divided into two main categories. The first focuses on finding only one global optimum, e.g., one peak, and the second focuses on finding multiple global optima, e.g., multiple peaks, and it is the focus of this work. The first category can be approached by single-solution and population-based metaheuristics, while in the second category, the use of population-based metaheuristics is best suited. Finding multiple global optima is more common in real-world optimization problems where there is no previous information about the number of globally optimal solutions in the search space landscape. Thus, this work proposes a self-adaptive Differential Evolution with DBSCAN algorithm and a two-step exploitation routine, named NCjDE-2LSar, applied to multiple global optima multimodal optimization. The jDE algorithm with Michalewicz mutation strategy is employed as a global optimizer. Candidate solutions are grouped in an external archive using the DBSCAN algorithm. External archive solutions represent possible peaks that will feed the Nelder–Mead and Hooke–Jeeves exploitation algorithms. The peak ratio is used as a performance metric. Six state-of-the-art algorithms are used to compare the results obtained by the proposed approach. Results show that the proposed algorithm outperforms the state-of-the-art algorithms concerning the mean absolute value of the peak ratio evaluation metric. The algorithm also achieved the perfect peak ratio (100%) in 10 out of 20 functions.

Software Methodology for Estimating the Efficiency of the Hardware Composition of Deep Packet Inspection System Using the Modernized Hooke ‒ Jeeves Method

Deep packet inspection systems on communication networks are used to identify the application generating a specific traffic flow. The issues related to modeling and design of deep packet inspection systems remain poorly understood. In this paper, a software technique for evaluating the effectiveness of the hardware composition of the servers of the deep packet inspection system is presented, using a mathematical model of such a system and software search methods. The description of the program search by the maximum element method and the Hook - Jeeves method is given. A modernization of the Hook-Jeeves method for a monotonically decreasing function is proposed. Comparison of the methods by the number of search steps is performed.