Problem-specific Information Research Articles

Improving the artificial bee colony algorithm with a proprietary estimation of distribution mechanism for protein–ligand docking

The protein–ligand docking problem plays an essential role in structure-based drug design. The challenge for a protein–ligand docking method is how to execute an efficient conformational search to explore a well-designed scoring function. In this study, we improved the artificial bee colony (ABC) algorithm and proposed an approach called ABC-EDM to solve the protein–ligand docking problem. ABC-EDM employs the scoring function of the classical AutoDock Vina to evaluate a solution during docking simulation. ABC-EDM adopts the search framework of the canonical ABC algorithm to execute conformational search. By further investigating the characteristics of the protein–ligand docking problem, a proprietary search mechanism inspired by estimation of distribution algorithm, i.e., estimation of distribution mechanism (EDM), is designed to enhance the performance of ABC-EDM. To verify the effectiveness of the proposed ABC-EDM, we compare it with three variants of the ABC algorithm, three evolutionary computation algorithms, and AutoDock Vina. The experimental results show that ABC-EDM can effectively solve the protein–ligand docking problem, and it can achieve a success rate 5% higher than AutoDock Vina on the GOLD dataset. This study reveals that taking advantage of problem-specific information about the protein–ligand docking problem to enhance a docking method contributes to solving this problem.

Finding the optimal exploration-exploitation trade-off online through Bayesian risk estimation and minimization

We propose endogenous Bayesian risk minimization (EBRM) over policy sets as an approach to online learning across a wide range of settings. Many real-world online learning problems have complexities such as action- and belief-dependent rewards, time-discounting of reward, and heterogeneous costs for actions and feedback; we find that existing online learning heuristics cannot leverage most problem-specific information, to the detriment of their performance. We introduce a belief-space Markov decision process (BMDP) model that can capture these complexities, and further apply the concepts of aleatoric, epistemic, and process risks to online learning. These risk functions describe the risk inherent to the learning problem, the risk due to the agent's lack of knowledge, and the relative quality of its policy, respectively. We demonstrate how computing and minimizing these risk functions guides the online learning agent towards the optimal exploration-exploitation trade-off in any stochastic online learning problem, constituting the basis of the EBRM approach. We also show how Bayes' risk, the minimization objective in stochastic online learning problems, can be decomposed into the aforementioned aleatoric, epistemic, and process risks.In simulation experiments, EBRM algorithms achieve state-of-the-art performance across various classical online learning problems, including Gaussian and Bernoulli multi-armed bandits, best-arm identification, mixed objectives with action- and belief-dependent rewards, and dynamic pricing, a finite partial monitoring problem. To our knowledge, it is also the first computationally efficient online learning approach that can provide online bounds on an algorithm's Bayes' risk. Finally, because the EBRM approach is parameterized by a set of policy algorithms, it can be extended to incorporate new developments in online learning algorithms, and is thus well-suited as the foundation for developing real-world learning agents.

Bulut Hesaplama İçin Evrimsel Algoritma Kullanarak İş Akışı Planlaması

Cloud computing provides powerful, highly scalable, flexible resources for real world applications. It also reduces the cost and operation expenses. Workflow scheduling is important for getting higher performance, reducing cost and using resources more efficiently in cloud computing. Workflow scheduling in cloud systems assigns tasks to resources available in the system and aims to utilize cloud resources by decreasing makespan of the workflow. In this study, an evolutionary algorithm is proposed to solve workflow scheduling problem. The main objective of this work is to minimize the makespan of the schedule. To achieve this goal, problem specific crossover operator and mutation operators are proposed in the evolutionary algorithm. The crossover operator will combine the problem-specific information stored in both parents to create a new individual. The mutation operators will explore neighbor solutions using some intelligent search mechanisms. This unique design of the operators increases the diversity of the search space and the quality of the solutions. As a result, the workflow schedules obtained from the evolutionary algorithm decreases the makespan of the workflow in the cloud system. The performance of the proposed study is measured using well-known scientific workflows and is compared with the algorithms from the literature. The proposed study outperforms all related algorithms in 67% of the test cases and obtains the same results in the remaining test cases.

A new hybrid-heuristic for large-scale combinatorial optimization: A case of quadratic assignment problem

Real world combinatorial optimization problems arise in very-large-scale. Solving such problems to optimality is impractical. In the past several decades, different heuristics and meta-heuristics have been proposed as solution procedure. In particular, to tackle the large-scale real applications, researchers have intelligently combined key components of competing methodologies to create superior solution procedures. In this study, we design a hybrid-heuristic (HH) using high-level solution algorithm framework that blends key components of three well-known meta-heuristics, scatter search, critical even tabu search, and genetic algorithm random keys, and applies to large-scale quadratic assignment problem (QAP). The HH has the potential to go beyond the goals of usual hybrid-heuristics, it potentially provides a framework for a self-adapted heuristic that can be applied to a wide range of problems with minimal inclusion of problem specific information. Extensive computational experiments applied to large-scale QAP instances from database of benchmark problems e.g., QAPLIB and taixxeyy collections, show that the proposed hybrid-heuristic (HH) provides an efficient approach to the large-scale QAP. The algorithm provides new best solutions for 61 out of 70 taixxeyy instances of large-scale QAP and finds the same best known solutions for QAPLIB and the 9 remaining instances from taixxeyy collections.

Co-Creating GIS-Based Dashboards to Democratize Knowledge on Urban Resilience Strategies: Experience with Camerino Municipality

Natural hazards are increasingly threatening our communities; hence it is imperative to provide communities with reliable information on possible impacts of such disasters, and on resilience measures that can be adopted to recover from disasters. To increase the engagement of various stakeholders in decision-making processes related to resilience to natural hazards, problem-specific information needs to be presented to them in a language understandable to non-experts in the field. To this end, this paper illustrates experimentation with low-code platforms for fast digitalization of resilience reports, incorporating the perspectives of various stakeholders in the analysis, thus making informed decision-making practicable. We present a co-creation-based approach to develop GIS-based user-friendly dashboards in support to the identification of resilience strategies against natural hazards; this approach has been developed within the framework of the European project ARCH. Urban areas are regarded as complex social-ecological systems whose various dimensions should be considered in this resilience endeavor, during all phases of the Disaster Risk Reduction and Climate Change Adaptation cycle. The work presented in this paper specifically targets the possible impacts and risks that might affect the cultural heritage subsystems of our cities, generally underrepresented in the international literature related to urban resilience assessment. We describe how we applied our approach to the Camerino municipality, a historic Italian town exposed to seismic risk, which was struck by a severe earthquake sequence in 2016–2017 and discuss the results of our experience.

Bayesian optimization techniques for high-dimensional simulation-based transportation problems

Bayesian optimization (BO) is an attractive method for tackling transportation optimization problems due to its ability to balance exploitation and exploration. However, scaling BO to solve high-dimensional problems is a major challenge which has remained unsolved. Since transportation problems can be high-dimensional, the use of BO to solve transportation problems has been limited. This paper explores the use of BO with Gaussian process (GP) models to tackle high-dimensional transportation problems. It proposes formulations of the prior mean function and covariance function of the GP that enable BO to incorporate problem-specific transportation information, while remaining computationally tractable. This is done through the use of an analytical surrogate model. We validate the method with the 1-D and 100-D Griewank functions. The impact of different forms of bias in the surrogate model was also evaluated using the 100-D Griewank function example. We then apply the method to a high-dimensional traffic signal control problem in New York City. The results indicate the added value of using the problem-specific information in the prior mean and/or the covariance function. Importantly, for surrogate models that are not accurate approximations of, but have significant (anti-)correlation with the true objective function, the better approach is to embed the information in the covariance function, rather than in the prior mean function. More generally, the use of problem-specific information in the covariance function is robust to the accuracy of the surrogate model. This opens the way for a variety of low-resolution analytical transportation models to be used to tackle high-dimensional simulation-based optimization problems.

Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists’ thinking process

In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper, we investigate whether machine learning can acquire explainable knowledge without directly introducing problem-specific information such as explicit physical mechanisms. In particular, a potential of machine learning to obtain the capability to identify a part of material structures that critically affects a physical property without human prior knowledge is mainly discussed. The guide for constructing the machine learning framework adopted in this paper is to imitate human researchers’ process of thinking in the interpretation and development of materials. Our framework was applied to the optimization of structures of artificial dual-phase steels in terms of a fracture property. A comparison of results of the framework with those of numerical simulation based on governing physical laws demonstrated the potential of our framework for the identification of a part of microstructures critically affecting the target property. Consequently, this implies that our framework can implicitly acquire an intuition in a similar way that human researchers empirically attain the general strategy for material design consistent with the physical background.

An effective implementation of reliability methods for Bayesian model updating of structural dynamic models with multiple uncertain parameters

The implementation of reliability methods in the framework of Bayesian model updating of structural dynamic models using measured responses is explored for high-dimensional model parameter spaces. The formulation relies on a recently established analogy between Bayesian updating problems and reliability problems. Under this framework, samples following the posterior distribution of the Bayesian model updating problem can be obtained as failure samples in an especially devised reliability problem. An approach that requires only minimal modifications to the standard subset simulation algorithm is proposed and implemented. The scheme uses an adaptive strategy to select the threshold value that determines the last subset level. Due to the basis of the formulation, the approach does not make use of any problem-specific information and, therefore, any type of structural model can be considered. The approach is combined with an efficient parametric model reduction technique for an effective numerical implementation. The performance of the proposed implementation is assessed numerically for a linear building model and a nonlinear three-dimensional bridge structural model. The results indicate that the proposed implementation represents an effective numerical technique to address high-dimensional Bayesian model updating problems involving complex structural dynamic models.

Job shop scheduling with genetic algorithm-based hyperheuristic approach

Job shop scheduling problems are NP-hard problems that have been studied extensively in the literature as well as in real-life. Many factories all over the world produce worth millions of dollars with job shop type production systems. It is crucial to use effective production scheduling methods to reduce costs and increase productivity. Hyperheuristics are fast-implementing, low-cost, and powerful enough to deal with different problems effectively since they need limited problem-specific information. In this paper, a genetic algorithm-based hyperheuristic (GAHH) approach is proposed for job shop scheduling problems. Twenty-six dispatching rules are used as low-level heuristics. We use a set of benchmark problems from OR-Library to test the proposed algorithm. The performance of the proposed approach is compared with genetic algorithm, simulating annealing, particle swarm optimization and some of dispatching rules. Computational experiments show that the proposed genetic algorithm-based hyperheuristic approach finds optimal results or produces better solutions than compared methods.

Lean Neural Networks for Autonomous Radar Waveform Design.

In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets and considerable processing power for both training and prediction. Some of these bottlenecks have been mitigated by recent increased availability of high-quality data sets, improvements in neural network development software, and greater hardware support. Due to algorithmic bloat, neural network inference times and imprecision make them undesirable for some problems where fast classical algorithm solutions already exist, other classes of algorithms, such as convex optimization, with non-trivial execution times could be reduced using neural solutions. These algorithms could be replaced with light-weight neural networks, benefiting from their high degree of parallelization and high accuracy when properly trained. Previous work has explored how low size, weight, and power (low SWaP) neural networks and neuromorphic computing can be used to improve autonomous radar waveform design techniques that currently rely on convex optimization. Autonomous radar waveform design helps meet the need for interference mitigation caused by an ever-growing number of consumer and commercial technologies which pollute the radio frequency (RF) spectrum. Spectral notching, a radar waveform design technique, augments transmitted radar waveforms to avoid frequencies with excessive interference while maintaining the integrity of the waveform. In this paper, we extend that work, demonstrating that lean neural networks and specialized hardware can improve inference time for waveform design without sacrificing accuracy. Our lean neural solution incorporates problem-specific information into the layer structures and loss functions to decrease network size and improve accuracy. We provide model outcomes implemented on radio frequency system on a chip (RFSoC) hardware that support our simulation results. Our neural network solution decreases inference time on traditional CPU hardware by 1057× and on GPU hardware accelerators by 883× while maintaining 99% cosine similarity.

Generalized Target Assignment and Path Finding Using Answer Set Programming

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications. We address this limitation by generalizing TAPF to allow for (1) unequal number of agents and tasks; (2) tasks to have deadlines by which they must be completed; (3) ordering of groups of tasks to be completed; and (4) tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple -- one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based methods can be efficient and can scale up to solve practical applications.

A discrete collaborative swarm optimizer for resource scheduling problem in mobile cellular networks

This study proposes a discrete collaborative swarm optimizer (DCCSO) for solving the resource scheduling problem in mobile cellular networks, which aims to employ minimum wireless bandwidth to meet various channel demands from each cell without violation of interference constraint. Many current algorithms can provide satisfactory solutions in dealing with simple problems, while some complex problems still need efficient scheduling schema, due to the limited resources. The proposed algorithm is inspired by the competitive swarm optimizer, whose superiority on continuous optimization problems has been proven by theory and verification. With the characteristics of the resource scheduling problem, the generalized order learning mechanism is designed, which updates the information of the loser particles by learning the sequential knowledge of the winners. Besides, plenty of invalid solutions will generate during the searching process in the original solution space degeneration of the exploration capability and coverage speed. To that end, an ensemble self-learning strategy is arisen by helping the neighborhood search by problem-specific information in the transformed solution space. The effectiveness of the proposed DCCSO is demonstrated on a set of real-world problems, and the experimental results show that the proposed algorithm exhibits better than or at least comparable performance to other state-of-the-art algorithms on most problems.

Deep neural networks architecture driven by problem-specific information

Deep learning provides a variety of neural network-based models, known as deep neural networks (DNNs), which are being successfully used in several domains to build highly accurate predictors. A key factor which usually makes DNNs to outperform traditional machine learning models is the amount of data that is nowadays accessible and available. Nevertheless, there are other factors linked to DNNs topologies that may also have influence on the predictive performance of DNN models. In particular, fully connected deep neural networks (fc-DNNs) typically struggle in achieving good performance rates when applied to small datasets. This is due to the high number of parameters which need to be learned when training this kind of models, which makes them prone to over-fitting issues. In this paper, authors propose the use of problem-specific information in order to impose constraints to network architecture so that a fc-DNN is transformed into a partially connected DNN (pc-DNN), in such a way that network topology is driven by prior knowledge. This work compares two baseline models, the elastic net and fc-DNNs, to pc-DNNs applied on three synthetic datasets with different number of samples. Synthetic data was generated to estimate the goodness of using problem-specific information to drive network architectures. Furthermore, a similar analysis is performed herein on a real-world problem dataset to show the benefits of pc-DNN models in term of predictive performance. The results of the analysis showed that pc-DNNs with built-in problem-specific information clearly outperformed the elastic net and fc-DNNs in most of the datasets used, in either synthetic or real-world problems. The pc-DNNs turned out to be a useful model, especially when it is applied to small- or medium-size datasets, on which it significantly outperformed the baseline models considered in this study. Specifically, the pc-DNNs achieved AUC and MSE improvement rates of ( $$8.21\%$$ , $$19.79\%$$ ) and ( $$6.65\%$$ , $$20.54\%$$ ) in small- and medium-size datasets for both case studies analyzed, the synthetic and real-world problem, respectively.

Incorporating shifting bottleneck identification in assembly line balancing problem using an artificial immune system approach

The manufacturing industry has evolved in the past few years due to the competitive global economy where the performance of its assembly line operations is primarily dependent upon optimum resource utilization. The assembly line operations are balanced among the available resources to obtain an equal amount of workload to achieve optimum resource utilization, called the assembly line balancing (ALB) problem. Various approaches have been proposed to solve the ALB problem, which is broadly categorized as exact, heuristic, and meta-heuristic approaches. Although solving the ALB problem is crucial, a bottleneck may still occur over the next operation stages. By using problem-specific information (bottleneck identification), it is expected to improve the solution quality of the ALB problem. As such, the contribution of this study is the computational method, namely as the swarm of immune cells with bottleneck identification (SIC+) approach, where both the ALB and bottleneck identification problems are addressed. In addition to the flexible problem representation, the SIC+ approach is equipped with a discrete bottleneck simulator to simulate the bottleneck scenario and bottleneck-specific operators to redistribute the machine workload of the identified bottleneck machine. The approach was tested on 24 benchmark data sets of the ALB problem, and the impact of incorporating bottleneck identification was illustrated. The experimental results show that the proposed SIC+ approach has achieved a total of 66.12% optimal solution over all instances of the benchmark data sets and has been compared with approaches from the literature where high-quality solutions were statistically justified.

Three dimensional (3D) imaging techniques in orthodontics-An update.

3D imaging is a technique which develops or creates the impression of depth within an image by deploying 2D data into 3-dimensional format. To aid in quality regulating processes for industrial purposes, 3D imaging has become an extremely valuable factor. Owing to their various drawbacks, a wide range of investigative methods formulated for demonstration of facial structures and the dentition were dilapidated. Currently in medicine, the most prevalent method is perhaps 3D imaging technique renders thorough and problem specific information regarding hard and the soft tissues, such as Computerized Tomography (CT), Cone Beam Computerized Tomography (CBCT), Micro Computerized Tomography (MCT), 3D laser scanning, structured light technique, stereophotogrammetry or 3D surface imaging systems (3dMD), 3D facial morphometry (3DFM), Tuned Aperture Computed Tomography (TACT), and Magnetic Resonance Imaging (MRI). 3D imaging techniques in orthodontics plays an important role by facilitating more elaborated diagnostic information on the precise cases like patients having craniofacial anomalies. Hence, the aim of this study was to review advances in 3D imaging with in the field of orthodontics.

Using a Spatio-Temporal Basis for ECG Imaging of Ventricular Pacings: Insights From Simulations and First Application to Clinical Data.

ECG imaging estimates the cardiac electrical activity from body surface potentials. As this involves solving a severly ill-posed problem, additional information is required to get a unique and stable solution. Recent progress is based on introducing more problem-specific information by exploiting the structure of cardiac excitation. However, added information must be either certain or general enough to not impair the solution. We have recently developed a method that uses a spatio-temporal basis to restrict the solution space. In the present work, we analyzed this method with respect to one of the most fundamental assumptions made during basis creation: cardiac (an)isotropy. We tested the reconstruction using simulations of ventricular pacings and then applied it to clinical data. In simulations, the overall median localization error was smallest with a basis including fiber orientation. For the clinical data, however, the overall error was smallest with an isotropic basis. This observation suggests that modeling priors should be introduced with care, whereby further work is needed.

Hyper-heuristic approaches for strategic mine planning under uncertainty

A hyper-heuristic refers to a search method or a learning mechanism for selecting or generating heuristics to solve computational search problems. Operating at a level of abstraction above that of a metaheuristic, it can be seen as an algorithm that tries to find an appropriate solution method at a given decision point rather than a solution. This paper introduces a new hyper-heuristic that combines elements from reinforcement learning and tabu search. It is applied to solve two complex stochastic scheduling problems arising in mining, namely the stochastic open-pit mine production scheduling problem with one processing stream (SMPS) and one of its generalizations, SMPS with multiple processing streams and stockpiles (SMPS+). The performance of the new hyper-heuristic is assessed by comparing it to several solution methods from the literature: problem-specific algorithms tailored for the two problems addressed in the paper and general hyper-heuristics, which use only limited problem-specific information. The computational results indicate that not only is the proposed new hyper-heuristic approach superior to the other hyper-heuristics, but it also provides results that are comparable to or improve on the results obtained by the state-of-the-art problem-specific methods.

Modeling spatially dependent functional data via regression with differential regularization

We propose a method for modeling spatially dependent functional data, based on regression with differential regularization. The regularizing term enables to include problem-specific information about the spatio-temporal variation of the phenomenon under study, formalized in terms of a time-dependent partial differential equation. The method is implemented using a discretization based on finite elements in space and finite differences in time. This non-tensor product basis allows to handle efficiently data distributed over complex domains and where the shape of the domain influences the phenomenon’s behavior. Moreover, the method can comply with specific conditions at the boundary of the domain of interest. Simulation studies compare the proposed model to available techniques for spatio-temporal data. The method is also illustrated via an application to the study of blood-flow velocity field in a carotid artery affected by atherosclerosis, starting from echo-color doppler and magnetic resonance imaging data.

An experimental study in community volunteers of the effects of focusing on views about seeking help for obsessional problems

People typically delay many years after developing OCD before they seek help. Factors linked to the decision whether to seek treatment have been identified. Decision-making literature suggests that outcomes of decision making are related to the factors which form the focus of attention and awareness. We evaluated whether focussing on enablers for treatment seeking has an impact on predictions of key treatment seeking behaviours in community controls, examining the likelihood of hypothetically seeking treatment. Participants (125) completed a focussing intervention where they were asked to rate how likely they would be to seek help before and after reading and rating for self-applicability specific information designed to focus their attention either on (a) ‘enabler’ factors taken from previous research or (b) on general information about OCD and its treatment.Findings indicated that focussing on either type of information increased the likelihood of hypothetically seeking treatment, with the enabler information facilitating greater increases in the likelihood of active treatment seeking.Health education efforts for those delaying seeking help for mental health difficulties such as OCD may benefit from more specific attention to increasing the salience of treatment enablers as well as problem specific information.

Establishing information seeking pathways in slow and flash floods

The purpose of this paper is to investigate information seeking behaviour of people involved in slow and flash flood disasters, specifically how they received the first alert, where they then turned for more information, and what their main sources and forms were. An online and mailed survey based on models of problem-specific information seeking and risk information for natural hazards secured responses from Australians who had experienced flash flood (n = 91) and slow flood (n = 41). It found that information pathways taken by individuals are different for slow and flash floods, but the set of information forms and sources used are similar. ‘Other people’, television and news and weather websites were predominant sources and forms in flash flood, and online sources, television and radio predominant in slow flood. The importance of other people and mainstream media (including their online sites) in information behaviour means that mainstream media should remain an important component of information efforts by agencies. This study builds further evidence that disaster type and the media landscape should be taken into account when developing warning and response communication strategies, and allows public information officers to prioritise communication forms during response.