Optimization Objective Function Research Articles

Optimization of the structure of the manufacturing industry of Ukraine according to the criteria of increasing the technology level of manufacturing operations and reducing their import dependence

Abstract The post-war reconstruction of Ukraine's economy should be based on new development priorities. First of all, we are talking about the structural transformation of the manufacturing industry, as a key segment of the economy, in the direction of increasing the weight of manufacturing operations with higher added value, socio-economic efficiency, the functioning of which will ensure the restoration of intersectoral relations, the reduction of import dependence, the improvement of the technical defence capability of the country and the social standards of life of the population. The purpose of the article is to optimize the structure of output and the structure of gross added value (GVA) of the manufacturing industry of Ukraine and the structure of intermediate consumption (in terms of domestic and imported components) of manufacturing operations. On the basis of the study of the relationships between the indicators of the economic efficiency of the Ukrainian manufacturing industry and the technological structure of its production (across 16 manufacturing operations), it was established that during the years 2013-2021, under the influence of non-economic factors, there was a significant decrease in the share of medium-high-tech industries (primarily chemical and machine-building) in the output of industry with a simultaneous decrease in the share of GVA in the output of these industries. On the basis of the obtained analytical information, using the author's methodical tools and the experience of previous studies, the optimization of the structure of output and GVA of the Ukrainian manufacturing industry was carried out according to the criterion of increasing the technology level. The objective function of such optimization was chosen to increase the efficiency of the manufacturing industry of Ukraine (expressed as an indicator of the share of GVA in output) to the level of Poland. According to the results of a detailed correlation analysis of the relationships between the values of indicators of import dependence and the economic efficiency of Ukrainian manufacturing operations, a number of negative features of the functioning of the latter were revealed, in particular: the deepening of import dependence of basic and strategic segments of industry, the increase in the volume of production of products based on give-and-take raw materials, etc. Based on the criterion of reducing import dependence of all manufacturing operations, but above all medium-high-tech ones, such a structure of intermediate consumption (in terms of imported and domestic components of costs) of manufacturing is modelled, under which the indicator of the share of GVA in the output of the manufacturing industry of Ukraine will correspond to the level of Poland. The modelling results form a scientific basis for strategic planning and forecasting the development of the manufacturing sector of the national economy. Prospective author's research in this direction will be aimed at the development of models for optimizing the structure of Ukrainian industry according to the criteria of increasing the innovativeness of industrial products and ensuring the socio-economic development of the country in the period of post-war recovery. Keywords industry, production, technology level, output, gross added value, efficiency, import dependence, intermediate consumption, optimization

Combustion efficiency control method of circulating fluidized bed boiler based on adaptive genetic algorithm

In order to improve the traditional boiler combustion efficiency control method, which has the problem of low efficiency after control, a new combustion efficiency control method of circulating fluidized bed boiler based on adaptive genetic algorithm is proposed in this paper. Firstly, the iterative principle of adaptive genetic algorithm is analyzed; Secondly, the combustion process model of circulating fluidized bed boiler is constructed by BP neural network method; Finally, based on the above model, the optimal control objective function of adaptive genetic algorithm is constructed, and the objective function is solved according to the iterative principle of adaptive genetic algorithm to complete the control of combustion efficiency of circulating fluidized bed boiler. The experimental results show that compared with the traditional control method, the combustion efficiency of the boiler controlled by this method is always more than 98%.

Reconfigurable integrated photonic filters for optical signal processing using a silicon photonic platform.

We present a systematic photonic filter design approach by deploying pole-zero optimization. The filter transfer function is derived from its specifications by formulating closed-form optimization objective functions and subsequently translating them into optical design parameters. Two distinct filter examples, namely Chebyshev and elliptic filters, are considered for the design and validation. A compact reconfigurable three-pole photonic filter is fabricated on a silicon photonic platform to illustrate the proposed design technique including transmission tunability. Integrated thermal phase shifters coupled with micro-ring resonators are used to reconfigure filter responses. A well-matched experimental demonstration is presented to validate the proposed tuning method. We achieved a sharp out-of-band edge rejection of at least 20 and 40 dB for the elliptic and Chebyshev filter, respectively.

Hybrid modeling and optimization of fiber laser hole cutting of austenitic stainless-steel sheets using response surface

In this study, an efficient approach was proposed to systematically model and optimize the laser small hole cutting process parameters using a hybrid approach for the design of experiment and multi-objective genetic algorithm optimization. The central composite design and response surface methodology were used to effectively model the impact of four main factors: cutting speed, laser power, gas pressure and focal distance on the responses. The responses considered were hole diameter circularity tolerance, spattering and cut kerf width, which were used to evaluate the quality of the laser hole cutting. The regression equations were used to model the effect of process parameters and their interactions on the responses. These regression models were then used as objective functions for optimization. The results show that the focal distance and laser power have had a significant influence on the hole diameter circularity tolerance and the variation in size of the cut kerf. In particular, the melted material spattering rate increased threefold when the focal distance increased from 0.4 to 0.8 mm. The optimization results highlighted that the best outcomes in terms of minimum deviation, spatter, and the cut-kerf width were achieved at low power (between 605 and 685 W) and low speeds (in the range of 11.1–12.7 m min−1). The optimal focal distance for all solutions was found to be 0 mm for the gas pressure (between 6.5 and 8 bars) to minimize the objective functions.

Dimensional synthesis of an Inspection Robot for SG tube-sheet

To ensure the operational safety of nuclear power plants, we present a Quadruped Inspection Robot that can be used for many types of steam generators. Since the Inspection Robot relies on the Holding Modules to grip the tube-sheet, it can be regarded as a hybrid robot with variable configurations, switching between 4-RRR-RR, 3-RRR-RR, and two types of 2-RRR-RR, and the variable configurations bring a great challenge to dimensional synthesis. In this paper, the kinematic model of the Inspection Robot in multiple configurations is established, and the analytical solution is given. The workspace mapping is analyzed by the solution-space, and the workspace of multiple configurations is decomposed into the workspace of 2-RRR to reduce the analysis complexity, and the workspace calculation is simplified by using the envelope rings. The optimization problem of the manipulator is transformed into the calculation of the shortest contraction length of the swing leg. The switching performance of the Inspection Robot is evaluated by stride-length, turning-angle, and workspace overlap-ratio. The performance indexes are classified and transformed based on the proportions and variation trends of dimensional parameters to reduce the number of optimization objective functions, and Pareto optimal solutions are obtained using an intelligent optimization algorithm.

Optimization of gearbox housing shape for agricultural UTV using structural–acoustic coupled analysis

In this study, gearbox radiated noise was successfully reduced through housing shape optimization. First, dynamic and structural–acoustic coupled analysis models of an agricultural UTV gearbox were developed. Then, the test equipment was configured to match that of the simulation model, and a test was conducted. The analysis and test results showed errors within 0.1 dB for vibration and 0.2 dB(A) for noise, indicating that the models were reliable. The housing design was then optimized using topology optimization based on the developed structural–acoustic coupling analysis model. The sound pressure level around the housing was used as an objective function for topology optimization. The optimal distribution of materials for the housing was also derived to reduce the radiated noise. Lastly, the housing rib was designed based on the optimization result, and an improvement in the radiated noise by approximately 2.43 dB(A) was confirmed in the operation area.

Maximizing acoustic band gap in phononic crystals via topology optimization

Designing phononic crystals (PnCs) to exhibit the widest attainable band gap, or to encompass a designated frequency range, is imperative for the realization of PnCs tailored to specific functionalities. However, achieving a wide band gap of acoustic waves centered around a specified frequency remains a significant challenge. The challenge arises from the fact that the optimization objective function may be enhanced by disconnected air regions. Nonetheless, these disconnected regions obstruct the circulation of air, rendering PnCs devoid of practical engineering utility. In this study, we propose a topology optimization methodology aimed at crafting air/solid PnCs that maximize the band gap of acoustic waves at a specified central frequency. To ensure adequate air permeability within the PnCs, the optimization model incorporates the virtual temperature technique in conjunction with the minimum length scale technique. The topology of the PnC unit cell is represented with a low number of design variables through the material-field series expansion, and the Kriging-based optimization algorithm is used to solve the complicated optimization problem. Diverse optimized configurations are presented, and experimental findings compellingly underscore the efficacy of the proposed optimization approach in the design of phononic band gap crystals geared towards acoustic waves at the specified central frequency.

Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production.

This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error (NRMSE) across nine (9) methods. In case the distribution of the data displayed in the user interface changes, the user should trigger the retrain of the AI models to ensure their accuracy and robustness. To form the objective function, the expert user should define the desired weight for each manipulated or non-manipulated variable through the user interface (UI), along with its corresponding constraints or target value. The user selects the variables involved in the objective function based on the optimization strategy, and the evaluation is based on the comparison of the optimized and the active value of the objective function. The differential evolution (DE) method optimizes the objective function that is formed by the linear combination of the selected variables. The results indicate that using DE improves the operation of both the cement mill and kiln, yielding a lower objective function value compared to the current values.

Design and Development of Machine Learning and Evolutionary Computation Methods for Risk Factors Identification in Early Childhood Disability

Timely identification of social and emotional disorders is crucial for the immediate welfare and future well-being of young children. The present study encounters challenges in identifying and establishing risk factors associated with early childhood impairment. Consequently, the overall system performance is substantially reduced. In order to tackle the aforementioned challenges, this study proposes the utilisation of the Cuckoo Search Optimisation with Adaptive Network-based Fuzzy Inference System (CSO+ANFIS) technique. The aim is to effectively generate and identify risk factors associated with early childhood disability. This study employs the CSO method to select the most important attributes and determines the optimal objective function based on the highest fitness values. The ANFIS technique focuses on identifying important risk variables by analysing the hidden layer and fuzzy inference values. The experimental results have shown that the suggested CSO+ANFIS technique surpasses the current paradigm in terms of accuracy and sensitivity metrics.

Convex optimization-based high-speed and security joint optimization scheme in optical access networks.

Data rate and security are essential performance metrics for passive optical networks (PON). However, existing optical access networks lack standardized metrics to evaluate rate and security performance uniformly. This paper introduces a high-speed and security joint optimization scheme for optical access networks using convex optimization. Evaluation metrics for data rate and security performance in PON are established. According to the evaluation metrics, the security optimization objective function Us, high-speed optimization objective function GMI, and high-speed security joint-optimization objective function Hs are established. An optimization problem is formulated to maximize weighted rate and security indicators, factoring in constraints such as maximum power, probability, amplifier capacity, normalized mutual information, and key and frame lengths. An alternating optimization method is applied to iteratively address sub-problems by exploiting successive convex approximations and differences of convex functions. This transforms non-convex sub-problems into convex optimizations. Experimental results highlight notable improvements in objective function values, confirming the effectiveness of the proposed high-speed security optimization algorithm for optical access networks.

Design Optimization of Blade Tip in Subsonic and Transonic Turbine Stages—Part II: Flow Physics and Augmented Aerothermal Integral Objective Function

Abstract In Part I, a companion paper of the two-part article, a subsonic turbine stage and a transonic one conditioned at the same Reynolds number, flow coefficient, loading coefficient and reaction, but two different exit Mach numbers are designed to provide a direct contrast between a high-subsonic and a transonic flow conditioning for rotor blade squealer tips. In this paper as Part II, further analyses are carried out to address the main issues of interest arising from Part I: first, to identify the driving flow physical mechanisms for the contrasting aerodynamic efficiency sensitivities of the two stages; second to seek a more suitable heat transfer objective function for the tip aerothermal design optimization, given the seemingly strong conflicts among those conventionally adopted heat transfer objective functions. Two counter-rotating tip vortical structures, the pressure side vortex (PSV) and the casing-driven cavity vortex (CCV), are shown to impact the aero-performance differently between the two stages. For the subsonic stage, the leakage flow is strongly affected by a stronger residual PSV at the squealer cavity exit. For the transonic stage however, the tip choking in limiting the over tip leakage (OTL) mass flow and favorable pressure gradient in a transonic flow over a separation bubble led to a much stronger and more persistent CCV and thus lower aero-effectiveness of squealer tip for the transonic stage. The two vortices also show major heat transfer signatures on the cavity surfaces by impingement. For the PSV, the impingement impact is mainly on the cavity floor. For the CCV, on the other hand, its impact is mainly on the inner side-wall of the suction side rim. The latter is found to be mainly responsible for the overall linear variations of the heat load with the squealer height. Again, the relative strength between PSV and CCV serves as an effective differentiator in the heat transfer performance. The stronger and more persistent CCV in the transonic stage results in a strong signature on a large portion of the suction side cavity inner sidewall, thus a much higher increment in heat transfer with the squealer height than that for the subsonic stage. In seeking to establish a more consistent heat transfer objective function for combined aerothermal design and optimization, a coolability weighted nonuniformity parameter is proposed to integrate the local heat transfer and the coolability. The proposed objective function is shown to lead to consistent Pareto fronts for the combined aerothermal performance sensitivities, particularly for the present cases with strong heat transfer nonuniformity which are particularly challenging to those conventional treatments as shown in Part I. The coolability-augmented objective function should thus serve as an enabler to help practical applications of blade tip aerothermal design optimizations.

Damped Vibration and Optimization of the Geometrical Parameters of FG Stiffened Cylindrical Shells Resting on Elastic Foundation Using GA

Functionally graded materials with their structural characteristics, such as the type of distribution, the size of phases, gradually change from one surface to another and provide a wide range of applications. In this research, the optimization of geometric parameters of FG structure rested on the elastic foundation, including damping effects is of interest. The cylindrical shell’s material properties are assumed to vary smoothly and continuously across the thickness according to the power law distribution of the volume fraction of constituents. The governing equations of stiffened functionally graded cylindrical shell resting on an elastic foundation are obtained using Hamilton’s principle, the first-order shear deformation theory and finite element method. The numerical results are obtained for studying the effect of various factors, such as distribution of volume fraction, length and thickness, different boundary conditions, stiffness and damping coefficients, and also the natural frequency of the dynamic responses of the cylindrical shell. The damping effects are considered which were not presented in previous studies. The frequency responses of the cylindrical FGM shell resting on elastic foundation were obtained by adding the damping term into the governing equations. Besides, analytical formulation results were considered as an objective function (input) for optimization in Genetic Algorithms. Finally, the compatibility of the analytical modeling results with the commercial finite element software was checked which showed good agreement.

Power Resource Allocation Algorithm for Dual-Function Radar–Communication System

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power.

An uncertainty-based objective function for hyperparameter optimization in Gaussian processes applied to expensive black-box problems

As of today, Gaussian processes (GPs) have been widely and successfully used in the context of design optimization based on expensive-to-evaluate functions. This supervised learning method enables a generation of accurate nonlinear surrogate models based on relatively small datasets. Nonetheless, their most valuable asset is to provide uncertainty in predictions. Despite their excellent stochastic properties, Gaussian processes are unfortunately not immune to threats such as the generation of distorted predictions, especially when the amount of data available is very limited. This shortcoming is caused by a poor choice of the GP hyperparameters and represents a serious threat to the efficiency and effectiveness of the whole surrogate-based optimization. In this paper we present the Hybrid Loss (HL), a novel uncertainty-aware objective function for the hyperparameter tuning of Gaussian processes. This method is intended to exploit information coming from the predictive variance to remedy the typical shortcomings of the log marginal likelihood, i.e. the objective function commonly used to optimize GP hyperparameters. By pairing this methodology with a well-known adaptive sampling strategy, we investigate the performance on a wide range of benchmark functions and a real engineering problem. The observed evidence clearly shows how uncertainty can be successfully exploited to make a wiser choice of the hyperparameters. This translates into more accurate predictions, surrogate models less prone to overfitting, and above all, greatly improved convergence rates.

Local Rényi entropy-based Gini index for measuring and optimizing sparse time-frequency distributions

This paper introduces a novel localized Gini index (GI) designed for the assessment and optimization of time-frequency distributions (TFDs). This approach employs the localized Rényi entropy (LRE) to quantify the local number of signal components, providing a unique perspective on interpreting time and frequency slice segments while considering the presence of auto-terms or cross-terms. The results demonstrate the effectiveness of the proposed LRE-based GI in overcoming the limitations of conventional concentration and sparsity measures, particularly in scenarios with varying component numbers and missing essential components that may arise during TFD reconstruction using the compressive sensing method. Furthermore, it proves as an efficient objective function in meta-heuristic optimization, ensuring the preservation of auto-terms and a significant enhancement in overall TFD quality. The performance of the proposed LRE-based GI is demonstrated on noisy synthetic and real-life signals.

Otsu Image Segmentation Based on a Fractional Order Moth–Flame Optimization Algorithm

To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image segmentation algorithm. Utilizing the advantages of memorability and heritability in fractional order differentiation, the position updating of moths is controlled by fractional order. Using the adaptive fractional order, the positions of moths are used to adjust the fractional order adaptively to improve the convergence speed. Combining the improved MFO algorithm with the two-dimensional Otsu algorithm, the optimization objective function is achieved by using its dispersion matrix. The experimental results indicate that, compared with traditional MFO, the convergence rate of the proposed algorithm is improved by about 74.62%. Furthermore, it has better segmentation accuracy and a higher fitness value than traditional MFO.

Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management.

Quasi-Static Force Analysis and Tooth Profile Modification Optimization of the Cycloid Speed Reducer

This article proposes a new method for optimizing tooth profile modification amounts of cycloidal reducers. By optimizing transmission error and transmission ratio fluctuation, the objective optimization function is determined, and the influence of optimized modification amount on various transmission performances is studied. In addition, this article also conducted mechanical analysis on the cycloidal reducer and obtained the results of tooth contact force. The mathematical model for the mechanical analysis of the cycloidal reducer was established, and the variation law of force distribution between the internal and external gears of the cycloidal reducer was calculated. The results indicate that the needle tooth force, bearing force, transmission ratio, and transmission error obtained through the mechanical analysis model using optimized modification quantities tend to stabilize and exhibit periodic changes, which corresponds to the actual scenario.

A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems

INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery.
 OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building.
 METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments.
 RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value.
 Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.

A comprehensive analysis and multi-objective optimization of a sustainable production system based on renewable energies

The adoption and promotion of clean and sustainable energy sources bring about significant benefits to the environment, economy, and society, both in the present and for future generations. This study introduces a pioneering solar-wind biomass multi-generation system, aimed at facilitating the sustainable production of electricity, fresh water, hydrogen, and heating. This system provides a comprehensive evaluation of the influence of environmental conditions and design factors on system efficiency, taking into account the crucial aspects of energy, exergy, economy, and environment (4E). Additionally, a meticulous sensitivity analysis is conducted to identify and prioritize key decision variables that exert the most significant impact on the overall system. Ultimately, the multi-objective optimization is conducted by taking into account various priorities in four distinct scenarios. This approach enables the simultaneous or individual optimization of the system's useful objective functions in relation to the total cost rate. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed to determine the ideal point that best fulfills the optimization criteria. By employing TOPSIS, this study aims to provide a robust and systematic decision-making framework for scenario selection in various domains. By evaluating the optimal points across various scenarios, the first scenario yields the most favorable outcomes in terms of the system's useful outputs. These include an exergy efficiency of 19.63 %, hydrogen production of 11.07 kg/h, fresh water production of 34.16 kg/s, and a total cost rate amounting to 213.80 $/h. Additionally, a considerable decrease of 22.65 % in the total cost rate is noted at the optimal point.