Constraint Optimization Techniques Research Articles

A traffic flow prediction method based on constrained dynamic graph convolutional recurrent networks

To enhance the efficiency of predicting future traffic flow trends within a transportation network, we propose a traffic flow prediction approach based on Constrained Dynamic Graph Convolutional Recurrent Network (C-DGCRN). Firstly, we employ matrix linear constraint and low-rank constraint optimization techniques to assist the Dynamic Graph Convolutional Recurrent Network (DGCRN) in extracting relevant internal correlations within the transportation network, leading to the formation of an affinity matrix. Secondly, we utilize the Hadamard product operation to effectively transfer weights from the affinity matrix to DGCRN, further enhancing its predictive accuracy. The experiments conducted an error assessment on the Metro Traffic Los Angeles and Performance Measurement System-Bay Area datasets. The Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error of C-DGCRN were 2.66, 5.06, and 6.83, respectively. Compared to state-of-the-art methods like Adaptive Graph Convolutional Recurrent Network, C-DGCRN showed a reduction of 7%, 9%, and 11% in these metrics.

P2BPO: Permeable Penalty Barrier-Based Policy Optimization for Safe RL

Safe Reinforcement Learning (SRL) algorithms aim to learn a policy that maximizes the reward while satisfying the safety constraints. One of the challenges in SRL is that it is often difficult to balance the two objectives of reward maximization and safety constraint satisfaction. Existing algorithms utilize constraint optimization techniques like penalty-based, barrier penalty-based, and Lagrangian-based dual or primal policy optimizations methods. However, they suffer from training oscillations and approximation errors, which impact the overall learning objectives. This paper proposes the Permeable Penalty Barrier-based Policy Optimization (P2BPO) algorithm that addresses this issue by allowing a small fraction of penalty beyond the penalty barrier, and a parameter is used to control this permeability. In addition, an adaptive penalty parameter is used instead of a constant one, which is initialized with a low value and increased gradually as the agent violates the safety constraints. We have also provided a theoretical proof of the proposed method's performance guarantee bound, which ensures that P2BPO can learn a policy satisfying the safety constraints with high probability while achieving a higher expected reward. Furthermore, we compare P2BPO with other SRL algorithms on various SRL tasks and demonstrate that it achieves better rewards while adhering to the constraints.

A Modified Water Cycle Algorithm: An Opposition Based Meta-Heuristic Optimization to Solve Real World Engineering Problems

This paper proposes the Opposition based learning on a latest recent population based Water Cycle Algorithm on different benchmark constraint optimization techniques. Water cycle is a Hydrological based technique which works on better search location of the stream and river that flows to the sea which works on certain control parameters that will be defined initially and obtain the population matrix. With the help of the application of the opposition learning opposite search will be made to receive the better search location to find the better fitness value and avoid the premature convergence and get best convergence rate. This Proposed Opposition based Water Cycle Algorithm is implemented and tested on fifteen benchmark problems mentioning the fitness value as well as the constraints value. The convergence plot using a comparative study between Water Cycle Algorithm and Opposition based Water Cycle Algorithm, the proposed method had proved to obtain the best result and superior for the problems on to which it had implemented. The ANOVA test result is shown for the statistical analysis of the data obtained.

Optimum-cost-based renewable energy chart considering micro-hydro, solar-PV, and hybrid systems using HOMER suitable for eastern Himalayan regions of India

<p><span lang="EN-US">The climatic change issues can only be addressed by adding more and more renewable energy into the energy chart for any region. However, the criteria for the composition of the chart shall be based on availability, cost-effectiveness, complementary nature, and optimally fit with the load curve. Based on the geographical diversity and availability of renewable energy in India, the energy chart is different for different regions. In this paper, an attempt has been made to prepare a renewable energy chart based on the equality constraint optimization technique for economical load sharing between the locally available renewable energy sources. The HOMER-based software platform has been utilized for cost analysis purposes. Based on locally available renewable energy sources, micro-hydro, and solar-PV are considered for analysis. In this paper, the electrical load of an educational institution situated in the eastern Himalayan region is considered for the case study. The data received from the case study has been utilized for the preparation of an appropriate and economically viable renewable energy chart for the mentioned region. Even though the analysis model applies to a particular region, it can be suitably modified for other regions also.</span></p>

Systems identification of servomechanism parameters using jellyfish, particle swarm and constraint optimization

In this paper, DC servomechanism parameters were identified offline using Jellyfish, particle swarm and constraint optimization techniques in a MATLAB simulation environment with experimental data. Specifically, the unknown parameters of the servomechanism were identified using a two-step approach. Initially, the first-order transfer function of the servomechanism which is characterized by a DC gain and time constant was determined analytically using the experimental open-loop speed step response of the servo motor. Next, by iterative minimization of a fitness score derived from the root mean squared error between the experimental and simulated position response of the servomechanism of an equivalent state-space model structure, the servomechanism parameters were identified. The simulated angular position step response of the servomechanism with the particle swarm, Jellyfish and constraint optimization algorithm, showed excellent agreement with the experimental data in descending order and was consistent with the fitness score of 1.9035, 0.0083, and 0.00706 respectively.

Random or heuristic? An empirical study on path search strategies for test generation in KLEE

Randomness may be the most straightforward strategy and has been widely used in software engineering. One criticism for random strategy is its aimless for tasks. Therefore, many heuristic strategies have been proposed to improve the random strategy. However, it is impossible to prove that heuristics are better than randomness in theory, especial for software test generation. This open question is always left to empirical study and researchers expect to conclude some guidelines in practice. This paper studies a key technique, path search strategies, of test generation. We conducted an empirical evaluation and comparison among ten concrete path search approaches provided by KLEE, where two approaches belong to random search strategy and eight belong to heuristic search strategy, with 53 GNU Coreutils applications. We also investigated both cases with and without constraint optimization techniques for KLEE-based test generation. The experimental results show that without optimization, one approach from random strategy – random-path – performs better than other techniques in terms of the number of completed paths, statement coverage and branch coverage. These results indicate that random strategy can be a better choice for test generation in most cases without optimization, and heuristic strategies need further investigation. However, when combined with optimization, the selection of search strategy depends on the specific optimization applied. We further analyze the reasons behind the statistical results and provide guidelines to select the appropriate path search strategy for test generation in practice.

Constrained neural network training and its application to hyperelastic material modeling

Neural networks (NN) have been studied and used widely in the field of computational mechanics, especially to approximate material behavior. One of their disadvantages is the large amount of data needed for the training process. In this paper, a new approach to enhance NN training with physical knowledge using constraint optimization techniques is presented. Specific constraints for hyperelastic materials are introduced, which include energy conservation, normalization and material symmetries. We show, that the introduced enhancements lead to better learning behavior with respect to well known issues like a small number of training samples or noisy data. The NN is used as a material law within a finite element analysis and its convergence behavior is discussed with regard to the newly introduced training enhancements. The feasibility of NNs trained with physical constraints is shown for data based on real world experiments. We show, that the enhanced training outperforms state-of-the-art techniques with respect to stability and convergence behavior within FE simulations.

Design of tuned liquid sloshing dampers using nonlinear constraint optimization for across-wind response control of benchmark tall building

The present study proposes an optimal design methodology of tuned liquid sloshing dampers (TLSDs) installed in wind-excited benchmark tall building. The TLSDs design parameters were determined using proposed optimal design methodology based on nonlinear constraint optimization technique. Top floor peak acceleration was considered as the objective function and maximum value of sloshing depth of liquid as a nonlinear constraint for the design purpose. The classical nonlinear shallow water wave theory was used for the simulation of liquid sloshing, and the basic equations of nonlinear liquid sloshing were solved using the Lax Finite Difference Scheme. The governing equations of motion of combined structure-TLSDs system were expressed as state-space variables and structural response was numerically simulated. The findings were presented and assessed on the basis of a comparison between power spectral density functions, time history analysis, root mean square, peak response, and performance criteria for benchmark building with and without TLSDs. The off-tuning effect of TLSDs due to ±15% uncertainty in the building stiffness was also investigated. Further, an attempt has been made to present the control effectiveness of TLSDs in time–frequency domain (wavelet scalogram) using continuous wavelet transformation technique. The optimal TLSDs design based on the proposed methodology was found to be quite effective in serviceability-based design of benchmark tall building under wind loads and can be very useful for the practical design of TLSDs.

Deep Learning Algorithm for Satellite Imaging Based Cyclone Detection

Satellite images are primary data in weather prediction modeling. Deep learning-based approach, a viable candidate for automatic image processing, requires large sets of annotated data with diverse characteristics for training purposes. Accuracy of weather prediction improves with data having a relatively dense temporal resolution. We have employed interpolation and data augmentation techniques for enhancement of the temporal resolution and diversifications of characters in a given dataset. Algorithm requires classical approaches during preprocessing steps. Three optical flow methods using 14 different constraint optimization techniques and five error estimates are tested here. The artificially enriched data (optimal combination from the previous exercise) are used as a training set for a convolutional neural network to classify images in terms of storm or nonstorm. Several cyclone data (eight cyclone datasets of a different class) were used for training. A deep learning model is trained and tested with artificially densified and classified storm data for cyclone classification and locating the cyclone vortex giving minimum 90% and 84% accuracy, respectively. In the final step, we show that the linear regression method can be used for predicting the path.

Coalition structure generation in cooperative games with compact representations

This paper presents a new way of formalizing the coalition structure generation problem (CSG) so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions to maximize social surplus. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than treating the function as a black box. Then we can solve the CSG problem more efficiently by directly applying constraint optimization techniques to this compact representation. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of CSG under these representation schemes. In this context, the complexity is driven more by the number of rules than by the number of agents. As an initial step toward developing efficient constraint optimization algorithms for solving the CSG problem, we also develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well.

Verification of Configuration Management Changes in Self-Organizing Networks

The verification of configuration management (CM) changes is an essential operation in a mobile self-organizing network (SON). Usually, a verification approach operates in three steps: it divides the network into verification areas, triggers an anomaly detection algorithm for those areas, and finally generates CM undo requests for the abnormally performing ones. Those requests set CM parameters to a previous stable state. However, the successful completion of this process can be quite challenging, since there are factors that might negatively impact its outcome. For instance, if a temporal degradation occurs during the optimization of a cell, a verification mechanism may wrongly assume that it is anomalous, and interrupt the optimization process. Furthermore, a verification strategy experiences difficulties when it faces verification collisions, i.e., conflicting undo requests that cannot be simultaneously deployed. At first, it has to determine whether the collision is a false positive one. Then, it has to resolve it by finding out which requests have the highest probability of restoring the network performance. In addition, it needs to consider the time that is given for rolling back CM changes. In this paper, we contribute to the area of SON verification by providing a solution that addresses those problems. Our approach makes use of constraint optimization techniques to resolve collisions as well as find the appropriate order for deploying undo requests. In addition, we use a minimum spanning tree-based clustering technique to eliminate false positive collisions. Further, we evaluate our solution in a simulation study in which we show its positive effect on the network performance.

An empirical study on constraint optimization techniques for test generation

Constraint solving is a frequent, but expensive operation with symbolic execution to generate tests for a program. To improve the efficiency of test generation using constraint solving, four optimization techniques are usually applied to existing constraint solvers, which are constraint independence, constraint set simplification, constraint caching, and expression rewriting. In this paper, we conducted an empirical study, using these four constraint optimization techniques in a well known test generation tool KLEE with 77 GNU Coreutils applications, to systematically investigate how these optimization techniques affect the efficiency of test generation. The experimental results show that these constraint optimization techniques as well as their combinations cannot improve the efficiency of test generation significantly for ALL-SIZED programs. Moreover, we studied the constraint optimization techniques with respect to two static metrics, lines of code (LOC) and cyclomatic complexity (CC), of programs. The experimental results show that the ``constraint set simplification technique can improve the efficiency of test generation significantly for the programs with high LOC and CC values. The ``constraint caching optimization technique can improve the efficiency of test generation significantly for the programs with low LOC and CC values. Finally, we propose four hybrid optimization strategies and practical guidelines based on different static metrics.

First-Stage Development and Validation of a Web-Based Automated Dietary Modeling Tool: Using Constraint Optimization Techniques to Streamline Food Group and Macronutrient Focused Dietary Prescriptions for Clinical Trials.

BackgroundStandardizing the background diet of participants during a dietary randomized controlled trial is vital to trial outcomes. For this process, dietary modeling based on food groups and their target servings is employed via a dietary prescription before an intervention, often using a manual process. Partial automation has employed the use of linear programming. Validity of the modeling approach is critical to allow trial outcomes to be translated to practice.ObjectiveThis paper describes the first-stage development of a tool to automatically perform dietary modeling using food group and macronutrient requirements as a test case. The Dietary Modeling Tool (DMT) was then compared with existing approaches to dietary modeling (manual and partially automated), which were previously available to dietitians working within a dietary intervention trial.MethodsConstraint optimization techniques were implemented to determine whether nonlinear constraints are best suited to the development of the automated dietary modeling tool using food composition and food consumption data. Dietary models were produced and compared with a manual Microsoft Excel calculator, a partially automated Excel Solver approach, and the automated DMT that was developed.ResultsThe web-based DMT was produced using nonlinear constraint optimization, incorporating estimated energy requirement calculations, nutrition guidance systems, and the flexibility to amend food group targets for individuals. Percentage differences between modeling tools revealed similar results for the macronutrients. Polyunsaturated fatty acids and monounsaturated fatty acids showed greater variation between tools (practically equating to a 2-teaspoon difference), although it was not considered clinically significant when the whole diet, as opposed to targeted nutrients or energy requirements, were being addressed.ConclusionsAutomated modeling tools can streamline the modeling process for dietary intervention trials ensuring consistency of the background diets, although appropriate constraints must be used in their development to achieve desired results. The DMT was found to be a valid automated tool producing similar results to tools with less automation. The results of this study suggest interchangeability of the modeling approaches used, although implementation should reflect the requirements of the dietary intervention trial in which it is used.

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.

Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.

Design and optimization of a novel throttling-inside-piston multi-stage hydraulic cylinder

In order to improve the performance of multi-stage cylinder to achieve rapid and steady extension, a novel throttling-inside-piston multi-stage hydraulic cylinder is proposed without changing physical size and piston working strokes of the traditional sequential multi-stage hydraulic cylinder. Based on internal structure optimal design and basic size parameter calculation, the mathematical model of the proposed throttling-inside-piston multi-stage hydraulic cylinder is studied using multi-rigid-body and impact-recovery dynamic theory. Then, the diameters of the orifices within the pistons are optimized using constraint optimization technique. Comparative simulation results show that the new type of throttling-inside-piston multi-stage hydraulic cylinder can significantly enhance the rapidity and steadiness for the erecting system.

Using induction coil sensor optimization techniques for designing compact geophysical transmitters

We have developed and tested code to optimise electromagnetic (EM) sensors to improve performance of the ARMIT B field induction coil sensor at desired frequencies. We aim to use the optimised parameters to develop a compact air core transmitter, which will form the basis for developing a compact ferromagnetic core transmitter. Techniques for optimising induction coil sensors are well established in literature and use analytical equations for the objective and constraint functions. Alternatives for EM sensor design are also well documented. In contrast, the design of compact transmitter systems needed for portability or in boreholes have limited discussion in the literature and have many more design constraints than sensors. Our ultimate intention is to use established sensor optimisation techniques to build a compact transmitter with sufficient magnetic dipole moment. To optimise an ARMIT induction current sensor we develop the algebraic expression for the total internal sensor noise to use as a constraint function. The objective function is the weight of the sensor. We aim to achieve noise goals of v / and v / frequencies of 1Hz and 2 kHz, respectively. 1 Hz was chosen because that is a common base-frequency for conductive sulphide exploration and 2 kHz was chosen as being appropriate for nuclear magnetic resonance investigations. We use numerical non-linear constraint optimization techniques to predict a target noise level of 1 pT at 1 Hz. , At this stage we predict the best 2 kHz sensor to have 4 fT noise at 2 kHz. This was based on existing dimensional and weight constraints on the induction coil sensor. We introduce an analogous method of transmitter optimisation using transmitter dipole moment as the objective function.

Optimization of three-loop missile autopilot gain under crossover frequency constraint

Abstract The open-loop crossover frequency is pointed as an important parameter for practical autopilot design. Since different gain designs may achieve the same open-loop crossover frequency, it should be neither considered as a performance objective of the optimal autopilot design-schemes nor neglected. Besides, the main assignment of the autopilot is to drive the missile to track the acceleration commands, so the autopilot gain design should be evaluated directly according to the resultant tracking performance. For this purpose, an optimal design methodology of the three-loop missile autopilot is introduced based on constraint optimization technique, where the tracking performance is established analytically as the design objective and the open-loop crossover frequency is formed as inequality constraint function, both are manipulated in terms of stable characteristic parameters of the autopilot closed-loop. The proposed technique is implemented with the assistance of a numerical optimization algorithm which automatically adjusts the design parameters. Finally, numerical simulation results are provided to demonstrate the effectiveness and feasibility of the proposed approach compared with that in some references.

Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint

A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm.

Strategic decision making on complex systems

In this paper, we propose a challenging research direction for Constraint Programming and optimization techniques in general. We address problems where decisions to be taken affect and are affected by complex systems, which exhibit phenomena emerging from a collection of interacting objects, capable to self organize and to adapt their behaviour according to their history and feedback. Such systems are unfortunately impervious to modeling efforts via state-of-the-art combinatorial optimization techniques. We provide some hints on approaches to connect and integrate decision making and optimization technology with complex systems via machine learning, game theory and mechanism design. In the first case, the aim is to extract modeling components to express the relation between global decisions and observables emerging from the real system, or from an accurate predictive model (e.g. a simulator). In the second case, the idea is to exploit game theory, mechanism design and distributed decision making to drive the process toward realistic equilibrium points avoiding globally optimal, but unrealistic, configurations. We conclude by observing how dealing with the complexity of the considered problems will require to greatly extend the capabilities of state of the art solvers: in this context, we identify some key issues and highlight future research directions.