Target Optimization Problem Research Articles

Multitasking optimization for the imaging problem in electrical capacitance tomography

Electrical capacitance tomography excels in measuring multiphase flows, but it faces challenges in reconstructing high-accuracy images. To tap into the potential of this measurement technique, we model the image reconstruction problem as a bilevel multitasking optimization problem, aiming to enhance imaging quality via effective knowledge transfer and integration across various tasks. This new imaging model consists of a target optimization problem and an auxiliary optimization problem, facilitating knowledge transfer and fusion between tasks. We design a new algorithm to solve the target optimization problem and integrate it with an autoencoder used for the cross-task knowledge transfer, forming a new multitasking optimizer to solve this proposed bilevel multitasking optimization imaging model. The extreme learning machine is developed for the prediction of learnable prior images by introducing a novel bilevel optimization framework that enables concurrent parameter selection and model training. A novel nested algorithm, designed to efficiently address optimization problems structured hierarchically, is developed to solve the training model. Evaluation results show that the new imaging algorithm outperforms popular imaging methods in terms of reconstruction accuracy and robustness, while also demonstrating stable performance. This proposed imaging algorithm achieves cross-task knowledge transfer and fusion, integrates measurement physics and machine learning, mines and integrates effective image priors, adaptively learns model parameters, alleviates the ill-posed property of the image reconstruction problem, increase the automation of the model, and improves imaging quality, robustness and performance stability, which provides a comprehensive solution to mitigate challenges in imaging.

Impartial Adversarial Distillation: Addressing Biased Data-Free Knowledge Distillation via Adaptive Constrained Optimization

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from a pretrained teacher to a light-weighted student without original training data. Existing works are limited by a strong assumption that samples used to pretrain the teacher model are balanced, which is, however, unrealistic for many real-world tasks. In this work, we investigated a pragmatic yet under-explored problem: how to perform DFKD from a teacher model pretrained from imbalanced data. We observe a seemingly counter-intuitive phenomenon, i.e., adversarial DFKD algorithms favour minority classes, while causing a disastrous impact on majority classes. We theoretically prove that a biased teacher could cause severe disparity on different groups of synthetic data in adversarial distillation, which further exacerbates the mode collapse of a generator and consequently degenerates the overall accuracy of a distilled student model. To tackle this problem, we propose a class-adaptive regularization method, aiming to encourage impartial representation learning of a generator among different classes under a constrained learning formulation. We devise a primal-dual algorithm to solve the target optimization problem. Through extensive experiments, we show that our method mitigates the biased learning of majority classes in DFKD and improves the overall performance compared with baselines. Code will be available at https://github.com/ldpbuaa/ipad.

Practical Methods of Optimizing the Maximum Range for Rockets

Range extension for artillery projectiles, rockets, and missiles is a problem of trajectory optimization based on a set of constraints aimed at maximizing the range as a measure of performance. Solid Rocket Motors (SRM) are rocket motors that use solid propellants. For higher engine efficiency and cost minimization, optimization of the engine is necessary, which implies a modification of the schematics of the engine. SRM optimization is currently a key topic in aerospace engineering research. Because some input data remain constant, others can be considered as variables, and their influence on the performance parameters of the rockets was investigated. In some investigations, the time until impact is an important performance parameter; in others, the range can be considered as the performance parameter to be optimized. In this study, pulsed solid propellant rocket motor technology was considered to demonstrate a feasible method for range extension. For the targeted optimization problem, the influence of the thrust profile along the launching angles was studied, and the results from the performed calculations were analyzed and discussed.

A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

Most of the energy consumption is now being used to supply human demand for electricity, which has increased the burden of power system planning to some extent, and thus, researchers proposed solutions to the optimal power flow (OPF) problem. In such a background, flexible AC transmission system (FACTS) devices are widely used in modern power systems to alleviate human power demand and improve network congestion problems. This research proposes a better-quality sine cosine algorithm (CPSCA) based on a crossover mechanism and pattern search algorithm for optimizing multiple objectives, such as system generation cost and transmission losses when we have multiple types of FACTS devices in the power system. The proposed method uses sine and cosine theory and evolutionary strategy to continuously sample and update the framework and improve the quality of the solutions. The method uses a class of vertical and horizontal crossover operators and pattern-shifting techniques to find the optimal solution set. In addition, the Weibull probability density function was used in this study to model uncertainty in wind energy, while direct costs, penalty costs, and standby costs were considered in constructing the target model. Various types of FACTS devices, such as thyristor-controlled series compensators, thyristor-controlled phase shifters, and static reactive power compensators, are also inserted into the model as power in the target optimization problem. The proposed algorithm has experimented on an IEEE30 bus test system. Based on the experimental results, CPSCA achieves an optimal total cost of 806.0225 $/h. In terms of power loss of the bus system, CPSCA reduces the total power loss value from 2.042 MW to 1.41 MW compared to the original sine cosine algorithm, which is a 31% improvement and has an optimal power loss value. Therefore, the simulation results demonstrate that the proposed algorithm is an effective technique for optimizing the optimal power flow of the entire power system and FACTS equipment.

Whiteness-based parameter selection for Poisson data in variational image processing

We propose a novel parameter selection strategy for variational imaging problems under Poisson noise corruption. The selection of a suitable value of the regularization parameter, which is crucial for achieving high quality reconstructions, is known to be a particularly hard task in low photon-counting regimes. In this work, we extend the so-called residual whiteness principle originally designed for additive white noise to Poisson data. The proposed strategy relies on exploiting the whiteness property of a suitably standardized Poisson noise process. After deriving the theoretical properties underlying our proposal, we solve the target optimization problem by the alternating direction method of multipliers, in its standard two-blocks version or in a semi-linearized version depending on the imaging problem. Our strategy is extensively tested on image restoration and computed tomography reconstruction problems, and compared to the state-of-the-art discrepancy principle for Poisson noise proposed by Zanella at al. as well as to a nearly exact version of it recently proposed by the authors.

An enhanced Forensic-Based Investigation algorithm and its application to optimal design of frequency-constrained dome structures

In this paper, an Enhanced Forensic-Based Investigation (EFBI) is proposed for optimal design of frequency-constrained dome-like trusses. The Forensic-Based Investigation (FBI) algorithm is a newly developed population-based metaheuristic inspired by the criminal investigation process. Throughout the FBI algorithm, the investigation and pursuit teams (i.e., the diversification and the intensification components of FBI, respectively) cooperate with each other to solve the target optimization problem. This study aims to modify the original formulation of FBI and propose an enhanced version of FBI, named as EFBI. The proposed EFBI seeks to reinforce communication between the investigation and pursuit teams with the aim of striking a better balance between the intensification and the diversification tasks. The performance of the proposed EFBI is compared with those of the standard FBI and some other optimization methods through three well-studied dome truss optimization problems with frequency constraints. To the best of our knowledge, this is the first time that FBI is applied to structural optimization. Results confirm that EFBI significantly outperforms the standard FBI, and has superior or comparable performance to other considered metaheuristics.

Machine Learning into Metaheuristics

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

Precise Burden Charging Operation During Iron-Making Process in Blast Furnace

The burden charging operation in blast furnace is one of the most important operations during iron-making process. In this paper, we focus on the study of precise burden charging operation, which involves two aspects: How to obtain and form the optimal burden surface shape. For the first problem, we construct a mapping model between the burden surface characteristic parameters and the comprehensive operational performance indicators of the blast furnace, and transform the search of optimal burden surface shape into the target optimization problem. The second problem refers to establishing a suitable burden charging strategy based on the basic burden surface and the optimal burden surface. In our work, by adaptively adjusting the opening degree of the throttle valve, it is possible to control accurate burden volume during the rotation of the charging chute, which can make sure to spill the appropriate burden volume on the suitable charging units. In the simulation of experiments, we collected the real industrial data during iron-making process and demonstrated the efficiency of the proposed model.

Double-Clutch Power Shift Quality Optimization Based on Optimal Control Theory

Highlights A power shift control strategy based on torque and speed transition, which aims to deliver multiple target and multiparameter optimization of power shift control, is proposed in this study. It can effectively solve the shift power cycle. Based on minimum optimal control theory, the optimal control of shift quality during power shifting optimizes clutch terminal oil pressure, which is determined by solving the Rebecca differential matrix equation and shift characteristics based on various stages. By aiming at the multiple target and multiparameter optimization problem of the clutch control in the power shift process, the minimum optimal control principle is applied to the shift quality optimization of the power shift. Based on the minimum optimal control theory, the optimal solution of the terminal oil pressure of the clutch is determined by solving the Rebecca differential matrix equation to improve the shift quality of the power shift process. Abstract . The dual clutch of the combined transmission of a tractor with large horsepower uses a dynamic shifting process, in which only one clutch undergoes slipping friction during the shift. A power shift control strategy based on torque and speed transition, which aims to deliver multiple target and multiparameter optimization of power shift control, is proposed in this study. Based on minimum optimal control theory, the optimal control of shift quality during power shifting optimizes clutch terminal oil pressure, which is determined by solving the Rebecca differential matrix equation and shift characteristics based on various stages. In addition, the power shift simulation model of the double clutch is established. Simulation results show that the power shift control strategy based on single slip friction can effectively avoid power flow cycle, uninterruptible tractor power shift, and adaptive resistance change. The minimum optimization theory can effectively reduce the output torque fluctuation in the dynamic shift process, reduce friction work, and improve the shift impact. Keywords: Double clutch, Heavy-horsepower tractor, Minimum theory, Power shift.

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem. Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable. This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals. On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model. The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective. In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings. And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained. In addition, the algorithm performance comparison is also completed. The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved. Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm's convergence speed and shows better evolutionary optimization ability.

Between hard and soft thresholding: optimal iterative thresholding algorithms

AbstractIterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target optimization problem. Surprisingly, we find that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees. Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators. Examples of this general class includes $\ell _q$ thresholding with appropriate choices of $q$ and a newly defined reciprocal thresholding operator. We also investigate the implications of the improved optimization guarantee in the statistical setting of sparse linear regression and show that this new class of thresholding operators attain the optimal rate for computationally efficient estimators, matching the Lasso.

Modeling Optimal Time Allocation for Work and Personal Life at The National Level (Case in Lithuania)

The article deals with the acute phenomenon in the 21st century – the distribution of time for work and personal life. Employed people in the modern society are most frequently faced with problems of work-life conflict. For this reason, this article primarily focuses on determination of optimal time allocation for work and personal life. First, this study analyzes the theoretical aspects of time allocation for work and personal life. Subsequently, a model for optimal time allocation for work and personal life is presented and the research methodology is substantiated. The model is realized by solving the multicriterial (target) optimization problem using weighting coefficients and priority methods and performing sensitivity analysis. This model is composed based on the employed population of Lithuania, but it can be adapted to other countries as well. In this case, the original questionnaire and time diary data should be obtained in the country under investigation. The results of the empirical research carried out showed that the population in Lithuania in 2017 begins to devote more time to personal life compared to 2003. Nevertheless, employed populations do not optimally allocate time between work and private life. For this reason, the article provides recommendations for employed people in Lithuania seeking to balance distribution of time for work and personal life, taking into consideration the current and desired monthly net wages.

Multiple fault diagnosis using factored evolutionary algorithms

When supporting commercial or defense systems such as aircraft avionics, guidance and control, electronic warfare, or propulsion systems, a perennial challenge is providing effective test and strategies to minimize downtime, thereby maximizing system availability. One can argue that one of the most effective ways to maximize downtime is to be able to detect and isolate as many faults that either exist or are emerging in a system at one time as possible. This is referred to as the multiplefault diagnosis problem, and it is a problem that is known to be computationally intractable (i.e., NP-complete) [1]. While several tools have been developed over the years to assist in addressing the multiple-fault problem, considerable work remains to provide the best possible given the information collected through observations, gripes, test results, and historical data. Recently, a new model for evolutionary computation has been developed called the Factored Evolutionary Algorithm (FEA) [2]. In FEA, a target optimization problem is broken down into subproblems that exhibit some kind of overlap. Then the optimization algorithm of choice (e.g., simulated annealing, genetic algorithm, particle swarm optimization) is applied to each of the subproblems, and the subproblems periodically share information with neighboring subproblems along the points of overlap.

Parametric optimization of circular compensatory holes in disc milliing cutters in SOLIDWORKS SIMULATION environment

The work is dedicated to improving the quality of design decisions during design automation structures through parametric optimization in SolidWorks Simulation environment for example circular compensation holes of disc milliing cutters. The possibility of increasing the dynamic stability of disc milliing cutters from cutting the force due to the impact on the frequency of natural oscillations has been determined. The paper proposed to provide increased frequency of natural oscillations of disc milliing cutters through the implementation of circular compensatory holes design parameters which are determined by the results of parametric optimization at SolidWorks Simulation environment. The features of formulation of engineering problems, conducted initial static strength and frequency analysis, according to which limits and target optimization problem. The effect of centrifugal forces on the natural frequencies holes disc milliing has been shown. Were comparable values of natural frequencies obtained using iterative and direct method of deciding SolidWorks Simulation according to the results of calculation. Optimization at SolidWorks Simulation environment were done using many repetitions (according to Box-Behnken plan) and thus the optimum design parameters of compensation openings holes disc milliing for these variables boundaries of research were obtained. The analysis of the impact of the number of holes and diameter circular array of a plurality of compensation holes on the importance of the first five frequencies of oscillation of holes disc milliing is made.

Path Planning for a Space-Based Manipulator System Based on Quantum Genetic Algorithm

In this study, by considering a space-based, n-joint manipulator system as research object, a kinematic and a dynamic model are constructed and the system’s nonholonomic property is discussed. In light of the nonholonomic property unique to space-based systems, a path planning method is introduced to ensure that when an end-effector moves to the desired position, a floating base achieves the expected pose. The trajectories of the joints are first parameterized using sinusoidal polynomial functions, and cost functions are defined by the pose deviation of the base and the positional error of the end-effector. At this stage, the path planning problem is converted into a target optimization problem, where the target is a function of the joints. We then adopt a quantum genetic algorithm (QGA) to solve this objective optimization problem to attain the optimized trajectories of the joints and then execute nonholonomic path planning. To test the proposed method, we carried out a simulation on a six-degree-of-freedom (DOF) space-based manipulator system (SBMS). The results showed that, compared to traditional genetic optimization algorithms, the QGA converges more rapidly and has a more accurate output.

A Modifier-Adaptation Strategy towards Offset-Free Economic MPC

We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem.

An improved hybrid optimization algorithm for vibration based-damage detection

In this paper, an improvement in the hybrid stochastic/deterministic Pincus-Nelder-Mead optimization algorithm (P-NMA) which enables to solve the target optimization problem of vibration-based damage detection is proposed. The proposed modification consists in reducing the sampling domain of the Pincus formula by assigning a maximum number of damaged elements, i.e., by allowing only a few elements of the sampling vector to be different from 1. Consequently, a new parameter which determines the maximum number of damaged elements (npmax) is introduced and must be choose by the designer. Such a modification attempts to speed up the convergence of the original version of the P-NMA and thus, reducing its computational cost. A series of numerical examples, all selected from literature, was performed. To test the accuracy and efficiency of the proposed improved optimization algorithm (IP-NMA), its results were compared to those obtained by the P-NMA and the metaheuristic harmony search algorithm (HS). A statistical analysis was also performed in order to test the robustness of the three algorithms. The proposed improved optimization algorithm showed better performance (more accurate and required lower computational cost than the original version of the P-NMA and the metaheuristic HS), emphasizing its capacity in damage diagnosis and assessment.

Two-phase anticipatory system design based on extended species abundance model of biogeography for intelligent battlefield preparation

This paper presents an extended model of biogeography based optimization (BBO) as opposed to the classical BBO wherein the HSI value of a habitat is not solely dependent upon the emigration and immigration rates of species but the HSI value is a function of different combinations of SIVs depending upon the characteristics of the habitat under consideration. The extended model also introduces a new concept of efforts required in migration from a low HSI solution to a high HSI solution for optimization in BBO. Hence, the proposed extended model of BBO presents an advanced optimization technique that was originally proposed by Dan Simon as BBO in December, 2008. Based on the concepts introduced in our extended model of BBO and its mathematics, we design a two – phase anticipatory system architecture for intelligent preparation of the battlefield which is the targeted optimization problem in our case. The proposed anticipatory system serves a dual purpose by predicting the deployment strategies of enemy troops in the battlefield and also finding the shortest and the best feasible path for attack on the enemy base station. Hence, the proposed anticipatory system can be used to improve the traditional approaches, since they lack the ability to predict the destination and can only find a suitable path to the given destination, leading to coordination problems and target misidentification which can lead to severe casualties. The designed system can be of major use for the commanders in the battlefield who have been using traditional decision making techniques of limited accuracy for predicting the destination. Using the above natural computation technique can help in enabling the commanders in the battlefield for intelligent preparation of the battlefield by automating the process of assessing the likely base stations of the enemy and the ways in which these can be attacked, given the environment and the terrain considerations. The results on two natural terrain scenarios that of plain/desert region of Alwar and hilly region of Mussourie are taken to demonstrate the performance of the technique where the proposed technique clearly outperforms the traditional methods and the other EAs like ACO, PSO, SGA, SOFM, FI, GA, etc. that have been used till date for path planning applications on satellite images with the smallest pixel count of 351 and 310 respectively. For location prediction application, the highest prediction efficiencies of the traditional method on Alwar and Mussourie was only 13% and 8% respectively as compared to the proposed method.

Optimal Machine-Tool Settings for the Manufacture of Face-Hobbed Spiral Bevel Gears

In this study, an optimization methodology is proposed to systematically define the optimal head-cutter geometry and machine-tool settings to simultaneously minimize the tooth contact pressure and angular displacement error of the driven gear (the transmission error), and to reduce the sensitivity of face-hobbed spiral bevel gears to the misalignments. The proposed optimization procedure relies heavily on the loaded tooth contact analysis for the prediction of tooth contact pressure distribution and transmission errors influenced by the misalignments inherent in the gear pair. The load distribution and transmission error calculation method employed in this study were developed by the author of this paper. The targeted optimization problem is a nonlinear constrained optimization problem, belonging to the framework of nonlinear programming. In addition, the objective function and the constraints are not available analytically, but they are computable, i.e., they exist numerically through the loaded tooth contact analysis. For these reasons, a nonderivative method is selected to solve this particular optimization problem. That is the reason that the core algorithm of the proposed nonlinear programming procedure is based on a direct search method. The Hooke and Jeeves pattern search method is applied. The effectiveness of this optimization was demonstrated on a face-hobbed spiral bevel gear example. Drastic reductions in the maximum tooth contact pressure (62%) and in the transmission errors (70%) were obtained.

Optimization for Centralized and Decentralized Cognitive Radio Networks

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield–Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.