Single Optimization Framework Research Articles

Robust optimization integrating aircraft trajectory and sequence under weather forecast uncertainty

Integration of trajectory optimization into sequence optimization is required for next-generation Arrival Managers (AMANs) to support Collaborative Decision-Making (CDM) and implementation of user-preferred 4D trajectories. In addition, considering uncertainty in the optimization is also necessary for making more robust decisions. To achieve these aims, this study proposes a method to integrate the trajectory and sequence of approach aircraft in a single optimization framework and calculate optimal robust solutions against weather forecast uncertainty. This uncertainty is quantified utilizing the ensemble weather forecast and the robust optimizations for trajectory and sequence are formulated in an ensemble approach. To connect the two optimizations, we introduce the so-called performance surfaces, which represent the characteristics of the optimal trajectory. The resulting integrated Trajectory and Sequence (T&S) optimization is a combination of the robust Optimal Control (OC) and Mixed-Integer Nonlinear Programming (MINLP). The MINLP problem is relaxed to the corresponding Nonlinear Programming (NLP) problem to reduce computational costs. In the case study, the trajectory and sequence are simultaneously optimized for two different objectives: the maximum throughput at the merging point and the minimum fuel burn while maintaining the inter-aircraft separation.

Multi-Service Network Slicing 5G NR Orchestration via Tailored HARQ Scheme Design and Hierarchical Resource Scheduling

In the radio access network domain, novel applications' requirements and network dynamics claim scalable processing capabilities, decentralized architecture, and on-demand resource allocation. Due to the intrinsic heterogeneous nature of the network resources, a fully context-aware fronthaul management is extremely complex to design. Operators have a high level-of-abstraction of the infrastructure's resources, which limits their decision-making capabilities. In this paper, a novel radio access network orchestrator is proposed, which jointly combines network function placement and resource allocation techniques in a single optimization framework. Given the infrastructure and users' delay constraints, we formulate a constrained linear maximization problem to determine the optimal timing parameters of the downlink hybrid automatic repeat scheme for each user. At inter-slice layer, our proposed resource allocation method is modelled as a Markov decision process, where the resources' sharing policy is tailored according to the real-time slice traffic load. At the intra-slice level, under mild conditions the radio resources are strategically prioritized, following a game theoretic model. Simulation results show that our method performs the hybrid automatic repeat request response 37%-88% compared to the classical LTE schemes. The dynamic allocation method at the inter-slice level improves the resource's utilization up to 50% compared to static network slicing. Moreover, at the intra-slice, the under and over resource provisioning has been decreased by 21.35% and 60.08%, respectively, compared to the classical fair resource allocation schemes.

Evolutionary Machine Learning With Minions: A Case Study in Feature Selection

Many decisions in a machine learning (ML) pipeline involve nondifferentiable and discontinuous objectives and search spaces. Examples include feature selection, model selection, and hyperparameter tuning, where candidate solutions in an outer optimization loop must be evaluated via a learning subsystem. Evolutionary algorithms (EAs) are prominent gradient-free methods to handle such tasks. However, EAs are known to pose steep computational challenges, especially when dealing with large-instance datasets. As opposed to prior works that often fall back on parallel computing hardware to resolve this big data problem of EAs, in this article, we propose a novel algorithm-centric solution based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evolutionary multitasking</i> . Our approach involves the creation of a band of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minions</i> , i.e., small data proxies to the main target task, that are constructed by subsampling a fraction of the large dataset. We then combine the minions with the main task in a single multitask optimization framework, boosting evolutionary search by using small data to quickly optimize for the large dataset. Our key algorithmic contribution in this setting is to allocate computational resources to each of the tasks in a principled manner. The article considers wrapper-based feature selection as an illustrative case study of the broader idea of using multitasking to speedup outer loop evolutionary configurations of any ML subsystem. The experiments reveal that multitasking can indeed speedup baseline EAs, by more than 40% on some datasets.

Robust Control for Non-Minimum Phase Systems with Actuator Faults: Application to Aircraft Longitudinal Flight Control

This study is concerned with developing a robust tracking control system that merges the optimal control theory with fractional-order-based control and the heuristic optimization algorithms into a single framework for the non-minimum phase pitch angle dynamics of Boeing 747 aircraft. The main control objective is to deal with the non-minimum phase nature of the aircraft pitching-up action, which is used to increase the altitude. The fractional-order integral controller (FIC) is implemented in the control loop as a pre-compensator to compensate for the non-minimum phase effect. Then, the linear quadratic regulator (LQR) is introduced as an optimal feedback controller to this augmented model ensuring the minimum phase to create an efficient, robust, and stable closed-loop control system. The control problem is formulated in a single objective optimization framework and solved for an optimal feedback gain together with pre-compensator parameters according to an error index and heuristic optimization constraints. The fractional-order integral pre-compensator is replaced by a fractional-order derivative pre-compensator in the proposed structure for comparison in terms of handling the non-minimum phase limitations, the magnitude of gain, phase-margin, and time-response specifications. To further verify the effectiveness of the proposed approach, the LQR-FIC controller is compared with the pole placement controller as a full-state feedback controller that has been successfully applied to control aircraft dynamics in terms of time and frequency domains. The performance, robustness, and internal stability characteristics of the proposed control strategy are validated by simulation studies carried out for flight conditions of fault-free, 50%, and 80% losses of actuator effectiveness.

Geometry and tool motion planning for curvature adapted CNC machining

CNC machining is the leading subtractive manufacturing technology. Although it is in use since decades, it is far from fully solved and still a rich source for challenging problems in geometric computing. We demonstrate this at hand of 5-axis machining of freeform surfaces, where the degrees of freedom in selecting and moving the cutting tool allow one to adapt the tool motion optimally to the surface to be produced. We aim at a high-quality surface finish, thereby reducing the need for hard-to-control post-machining processes such as grinding and polishing. Our work is based on a careful geometric analysis of curvature-adapted machining via so-called second order line contact between tool and target surface. On the geometric side, this leads to a new continuous transition between "dual" classical results in surface theory concerning osculating circles of surface curves and osculating cones of tangentially circumscribed developable surfaces. Practically, it serves as an effective basis for tool motion planning. Unlike previous approaches to curvature-adapted machining, we solve locally optimal tool positioning and motion planning within a single optimization framework and achieve curvature adaptation even for convex surfaces. This is possible with a toroidal cutter that contains a negatively curved cutting area. The effectiveness of our approach is verified at hand of digital models, simulations and machined parts, including a comparison to results generated with commercial software.

Progressive low-rank subspace alignment based on semi-supervised joint domain adaption for personalized emotion recognition

Recently, many scenarios, such as affective disorders treatment, have sparked rising needs for establishment of personalized emotion recognition (PER) models. Unfortunately, the data sparsity issue violates the basic i.i.d. assumption of supervised learning (i.e., training data and test data are independently and identically distributed). In this paper, we present a semi-supervised joint domain adaption (SSJDA) solution, aiming to inject the hidden domain knowledge from ample labeled data of multiple source individuals into the target subject’s customized model. Specifically, we put forward a novel Progressive Low-Rank Subspace Alignment (PLRSA) approach, which unifies a semi-supervised instance-transfer paradigm and an unsupervised mapping-transfer learning paradigm in a single optimization framework. We leverage the boosting-based TrAdaBoost algorithm and the Transfer Component Analysis (TCA) algorithm for the implementation of instance reweighting and feature matching, respectively. Then we introduce the ℓ2,1- norm to pass feedback and make the joint learning feasible. The central idea is to progressively minimize the cross-domain distribution discrepancies to finally construct the optimal domain-invariant features. We systematically compare the PLRSA method with five state-of-the-art techniques using two public EEG datasets (DEAP and SEED). Both many-to-one and one-to-one evaluations are performed. The experimental results have confirmed the efficacy of the proposed method.

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions.

A convex programming solution for gate-sizing with pipelining constraints

This paper proposes a novel geometric programming based formulation to solve a gate-sizing and retiming problem in the context of circuit optimization. The gate-sizing aspect of the problem involves continuous variables while the retiming problem involves the placement of registers in the circuit and can be naturally modeled using discrete variables. Our formulation is solved using first-order convex programming. We show promising experimental results on industrial circuits. We also investigate formally the computational complexity of the problem. To our knowledge, this is the first effort that solves this problem in a single optimization framework.

Evolutionary multi-objective optimization based overlapping subspace clustering

• It reports the first attempt in integrating multi-objective optimization and generation of overlapped subspace clusters. • The proposed method defines a new objective function in order to select the optimum subspace feature set for each cluster. • To allow the overlapping of objects, an objective function is defined that optimizes the membership degree. • Mutation operators that have been used in this approach are the modified versions of those used in the method ChameleoClust. • The proposed subspace clustering method is applied to a real-life application of bi-clustering the gene expression data . Subspace clustering techniques divide the data set into various groups, where each group is represented by a subset of features known as subspace feature set, that are relevant to the objects in the group. The grouping is performed in such a way that similar objects are placed in the same group, whereas dissimilar objects are in different groups. Most of the previous subspace clustering methods have not considered an object to be a part of more than one cluster. However, in many real-life situations, an object may belong to more than one cluster. Moreover , subspace clustering algorithms developed in the past are based on single objective optimization framework which limits in optimizing only a particular shape or property of the clusters. To this end, we have developed an evolutionary-based overlapped subspace clustering method using multi-objective optimization framework. Various mutation operators have been used to explore the search space effectively. Multiple objectives that have been optimized simultaneously in this algorithm are ICC-index, MNR-index and PSM-index. The developed algorithm is evaluated with 7 real-life and 16 synthetic data sets. However, to check the efficiency of using multiple objectives, the proposed algorithm is also tested with 3 big data sets. An application of the proposed method is shown in bi-clustering the gene expression data. The results obtained using these 23 data sets and 3 big data sets are compared with many state-of-the-art algorithms. The comparative study illustrates the efficacy of the proposed algorithm over state-of-the-art algorithms.

Reinforcement-Learning-Based Query Optimization in Differentially Private IoT Data Publishing

With the advancement of Internet of Things (IoT) and computing paradigms, massive data are collected and processed to enhance intelligent applications. However, by deliberately sending some queries, an attacker may be able to derive the sensitive information of IoT data owners. To prevent privacy leakage during IoT data query, differential privacy (DP) hides private information by introducing noise to the query results. As DP introduces randomized noise that will affect query accuracy (data utility), the tradeoff between privacy preservation and data utility is a challenge. In this article, we first propose a novel optimization framework for single query to minimize the privacy cost, while satisfying both personalized DP and customized data utility. We design a reinforcement learning-based algorithm for single query optimization framework (SQOF_RL) to solve the optimization problem efficiently. Then, we propose a SQOF_RL and SVT-based batch query optimization mechanism (S2BQOM) to answer more queries privately. The performance evaluation shows that SQOF_RL and S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BQOM can effectively optimize single query and batch queries in terms of privacy cost, data utility, personalized privacy, and query satisfaction. Finally, the performance analysis reveals that our work can be applied to multiple linear/nonlinear query functions instead of one particular query function.

Impact of renewable resource quality on security of supply with high shares of renewable energies

Globally, renewable energy sources (RES) are getting more and more competitive even without subsidies. In general, optimization methods are used to identify the most economic setup of individual power systems. This study contributes to the discussion on how much reserve capacity a power system should have to ensure reliable electricity supply in assessing the explicit and probabilistic system reliability metric loss of load hours (LOLH) as well as expected energy not served (EENS) within a dynamic programming approach. Multi-year RES profiles from different locations are used to identify the minimum reserve margin (RM) requirements using LOLH and EENS as planning criterions. The findings indicate, that using RM as the only reliability constraint within optimization is not appropriate as a too high assumption on RM would increase the required conventional generation capacity unnecessarily and a too low assumption would risk reliable power supply. Using LOLH as the single metric for reliable power system planning, the EENS would grow with increasing RES contribution. This is the result due to the concept of LOLH as the amount of electricity not supplied is not part of the metric, only the hours of power undersupply are. On the other hand, a constant assumption of EENS is misleading as well as the concept of EENS does not consider the number of hours the power service can’t be fulfilled. Therefore, the recommendation is to use LOLH and EENS simultaneously in a single optimization framework as shown within this study.

ProgSIO-MSA: Progressive-based single iterative optimization framework for multiple sequence alignment using an effective scoring system.

Aligning more than two biological sequences is termed multiple sequence alignment (MSA). To analyze biological sequences, MSA is one of the primary activities with potential applications in phylogenetics, homology markers, protein structure prediction, gene regulation, and drug discovery. MSA problem is considered as NP-complete. Moreover, with the advancement of Next-Generation Sequencing techniques, all the gene and protein databases are consistently loaded with a vast amount of raw sequence data which are neither analyzed nor annotated. To analyze these growing volumes of raw sequences, the need of computationally-efficient (polynomial time) models with accurate alignment is high. In this study, a progressive-based alignment model is proposed, named ProgSIO-MSA, which consists of an effective scoring system and an optimization framework. The proposed scoring system aligns sequences effectively using the combination of two scoring strategies, i.e. Look Back Ahead, that scores a residue pair dynamically based on the status information of the previous position to improve the sum-of-pair score, and Position-Residue-Specific Dynamic Gap Penalty, that dynamically penalizes a gap using mutation matrix on the basis of residue and its position information. The proposed single iterative optimization (SIO) framework identifies and optimizes the local optima trap to improve the alignment quality. The proposed model is evaluated against progressive-based state-of-the-art models on two benchmark datasets, i.e. BAliBASE and SABmark. The alignment quality (biological accuracy) of the proposed model is increased by a factor of 17.7% on BAliBASE dataset. The proposed model's efficiency is compared with state-of-the-art models using time complexity as well as runtime analysis. Wilcoxon signed-rank statistical test results concluded that the quality of the proposed model significantly outperformed progressive-based state-of-the-art models.

ISA: a hybridization between iterated local search and simulated annealing for multiple-runway aircraft landing problem

This paper presents an efficient method for aircraft landing problem (ALP) based on a mechanism that hybridizes the iterated local search (ILS) and simulated annealing (SA) algorithms. ALP is handled by scheduling each incoming aircraft to land on a runway in accordance with a predefined landing time frame. The main objective to address is to find a feasible aircraft scheduling solution within the range of target time. The proposed hybridization method complements the advantages of both ILS and SA in a single optimization framework, referred to as iterated simulated annealing (ISA). The optimization framework of ISA has two main loops: an inner loop and an outer loop. In the inner loop, SA is utilized through a cooling schedule and a relaxing acceptance strategy to allow ISA to escape the local optima. In the outer loop, the restart mechanism and perturbation operation of the standard ILS are used to empower ISA to broadly navigate different search space regions. Extensive evaluation experiments were conducted on thirteen small- and large-sized ALP instances to assess the effectiveness and solution quality of ISA. The obtained results confirm that the proposed ISA method considerably performs better than other state-of-the-art methods in which ISA is capable of reaching new best results in 4 out of 24 large-sized problem instances as well as obtaining the best results in all small-sized instances. Evaluation on large-sized instances confirms a high degree of performance. As a new line of research, ISA is an effective method for ALP which can be further investigated for other combinatorial optimization problems.

Collision-Free Video Synopsis Incorporating Object Speed and Size Changes.

This paper presents a new surveillance video synopsis method which performs much better than previous approaches in terms of both compression ratio and artifact. Previously, a surveillance video was usually compressed by shifting the moving objects of that video forward along the time axis, which inevitably yielded serious collision and chronological disorder artifacts between the shifted objects. The main observation of this paper is that these artifacts can be alleviated by changing the speed or size of the objects, since with varied speed and size the objects can move more flexibly to avoid collision points or to keep chronological relationships. Based on this observation, we propose a video synopsis method that performs object shifting, speed changing, and size scaling simultaneously. We show how to integrate the three heterogeneous operations into a single optimization framework and achieve high-quality synopsis results. Unlike previous approaches that usually use alternative optimization strategies to solve synopsis optimizations, we develop a Metropolis sampling algorithm to find the solution for our three-variable optimization problem. A variety of experiments demonstrate the effectiveness of our method.

Effective Video Stabilization via Joint Trajectory Smoothing and Frame Warping.

Video stabilization is usually composed of three stages: feature trajectory extraction, trajectory smoothing, and frame warping. Most previous approaches view them as three separate stages. This paper proposes a method combining the last two stages, namely the trajectory smoothing and frame warping stages, into a single optimization framework. The novelty exists in the way of how we combine them: the trajectory smoothing part plays a major role while the frame warping part plays an auxiliary role. With this kind of design, we can conveniently increase the strength of the trajectory smoothing part by a robust first-order derivative term, which makes it possible to produce very aggressive stabilization effects. On the other hand, we adopt adaptive weighting mechanisms in the frame warping part, to follow the smoothed trajectories as much as possible while regularizing other places as similar as possible. Our method is robust to utilize both foreground and background features, and very short trajectories. The utilization of all these information in turn increases the accuracy of the proposed method. We also provide a simplified implementation of our method, which is less accurate but more efficient. Experiments on various kinds of videos demonstrate the effectiveness of our method.

A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer

Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization context, ELD is formulated as a non-convex, nonlinear, constrained optimization problem which cannot be easily solved using calculus-based techniques. Several optimization algorithms have been adapted. Due to the complexity nature of ELD search space, the theoretical concepts of these optimization algorithms have been modified or hybridized. In this paper, the grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with $$\beta$$ -hill climbing optimizer ( $$\beta$$ HC) which is a local search algorithm, to improve convergence properties. GWO is very powerful in a wide search, while $$\beta$$ HC is very powerful in deep search. By combining the wide and deep search ability in a single optimization framework, the balance between the exploration and exploitation is correctly managed. The proposed hybrid algorithm is named $$\beta$$ -GWO which is evaluated using five different test cases of ELD problems: 3 generating units with 850 MW; 13 generating units with 1800 MW; 13 generating units with 2520 MW; 40 generating units with 10,500 MW; and 80 generating units with 21,000 MW. $$\beta$$ -GWO is comparatively measured using 49 comparative methods. The results obtained by $$\beta$$ -GWO outperform others in most test cases. In conclusion, the proposed $$\beta$$ -GWO is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain.

Virtual Prototyping of Axial Piston Machines: Numerical Method and Experimental Validation

This article presents a novel methodology to design swash plate type axial piston machines based on computationally based approach. The methodology focuses on the design of the main lubricating interfaces present in a swash plate type unit: the cylinder block/valve plate, the piston/cylinder, and the slipper/swash plate interface. These interfaces determine the behavior of the machine in term of energy efficiency and durability. The proposed method couples for the first time the numerical models developed at the authors’ research center for each separated tribological interface in a single optimization framework. The paper details the optimization procedure, the geometry, and material considered for each part. A physical prototype was also built and tested from the optimal results found from the numerical model. Tests were performed at the authors’ lab, confirming the validity of the proposed method.

An efficient method for clustered multi-metric learning

Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one. More specifically, the training examples are divided into several disjoint clusters, in each of which a distance metric is trained to separate the data locally. Additionally, a global regularization is introduced to preserve some common properties of different clusters in the learned metric space. By learning multiple distance metrics jointly within a single unified optimization framework, our method consistently outperforms single distance metric learning methods, while being more efficient than other state-of-the-art multi-metric learning methods.

Key generation for plain text in stream cipher via bi-objective evolutionary computing

Evolutionary algorithms are widely used to solve a wide variety of continuous, discrete and combinatorial optimization problems. Evolutionary multi-objective optimization problems seek Pareto front in order to negotiate the trade-off amongst various objective functions present in the problem. Much of the literature on cryptography focuses on making the inference problem harder, for securing the content. In this paper, we developed key generation algorithms using Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) in the bi-objective optimization framework and Improved Modified Harmony Search + Differential Evolution (IMHS+DE), Differential Evolution (DE) and Improved Modified Harmony Search (IMHS), in the single objective optimization framework. For encoding the keystream thus generated as well as the plain text we employed the Mutated Huffman Tree Coding algorithm. In the next phase, we encrypted the encoded keystream as well as the encoded plain text in order to generate the cipher text. We then decrypted the cipher text using the encoded key stream followed by the decoding of the deciphered text using the code tables. Following the literature, we generated random texts of varying lengths and code table sizes in order to demonstrate the effectiveness of our proposed method. The proposed algorithms are compared with the extant methods. In the case of bi-objective optimization set up, we also plotted the empirical attainment function (EAF) surface to summarize the effectiveness of the NSGA-II based key generation algorithm. Of particular significance is the highest entropy value yielded by the NSGA-II based algorithm, which in turn indicates the strength of the key generated by the NSGA-II.

Integration of order picking and vehicle routing in a B2C e-commerce context

E-commerce sales are increasing every year and customers who buy goods on the Internet have high service level expectations. In order to meet these expectations, a company’s logistics operations need to be performed carefully. Optimizing only internal warehouse processes will often lead to suboptimal solutions. The interrelationship between the order picking process and the delivery process should not be ignored. Therefore, in this study, an order picking problem and a vehicle routing problem with time windows and release dates are solved simultaneously using a single optimization framework. To the best of our knowledge, it is the first time that an order picking problem and a vehicle routing problem are integrated. A mixed integer linear programming formulation for this integrated order picking-vehicle routing problem (OP-VRP) is provided. The integrated OP-VRP is solved for small instances and the results are compared to these of an uncoordinated approach. Computational experiments show that integration can lead to cost savings of 14% on average. Furthermore, higher service levels can be offered by allowing customers to request their orders later and still get delivered within the same time windows.