Greedy Approach Research Articles

Enhanced sine cosine algorithm with crossover: A comparative study and empirical analysis

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA.

Identification of piecewise affine model for batch processes based on constrained clustering technique

In this paper, a novel identification method of piecewise affine (PWA) model for batch processes based on constrained clustering technique is proposed. In traditional clustering-based identification approaches, data classification and region partition are performed individually so that inseparable problem usually occurs in the partition phase. The proposed method uses a constrained K-means clustering algorithm to simultaneously perform both data classification and region partition, which is accomplished by imposing the complete and non-overlapping partition constraints into the clustering optimization problem. We employ a greedy iterative approach combined with the golden section search to efficiently solve the constrained clustering problem. This method can greatly improve the accuracy of the identified PWA model. Finally, we demonstrate the effectiveness of the proposed identification method.

Environmental Stability of Crystals: A Greedy Screening.

Discovering materials that are environmentally stable and also exhibit the necessary collection of properties required for a particular application is a perennial challenge in materials science. Herein, we present an algorithm to rapidly screen materials for their thermodynamic stability in a given environment, using a greedy approach. The performance was tested against the standard energy above the hull stability metric for inert conditions. Using data of 126 320 crystals, the greedy algorithm was shown to estimate the driving force for decomposition with a mean absolute error of 39.5 meV/atom, giving it sufficient resolution to identify stable materials. To demonstrate the utility outside of a vacuum, the in-oxygen stability of 39 654 materials was tested. The enthalpy of oxidation was found to be largely exothermic. Further analysis showed that 1438 of these materials fall into the range required for self-passivation based on the Pilling–Bedworth ratio.

Dynamic resource allocation for controlling pathogen spread on a large metapopulation network.

To control the spread of an infectious disease over a large network, the optimal allocation by a social planner of a limited resource is a fundamental and difficult problem. We address this problem for a livestock disease that propagates on an animal trade network according to an epidemiological–demographic model based on animal demographics and trade data. We assume that the resource is dynamically allocated following a certain score, up to the limit of resource availability. We adapt a greedy approach to the metapopulation framework, obtaining new scores that minimize approximations of two different objective functions, for two control measures: vaccination and treatment. Through intensive simulations, we compare the greedy scores with several heuristics. Although topology-based scores can limit the spread of the disease, information on herd health status seems crucial to eradicating the disease. In particular, greedy scores are among the most effective in reducing disease prevalence, even though they do not always perform the best. However, some scores may be preferred in real life because they are easier to calculate or because they use a smaller amount of resources. The developed approach could be adapted to other epidemiological models or to other control measures in the metapopulation setting.

Minimization of Number of Tool Switching Instants in Automated Manufacturing Systems

This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and due to limited tool magazine capacity, and because it is not possible to load all available tools on the machine, tools must be switched. The ultimate goal, in this framework, is to minimize the total number of tool switching instants. We provide a mathematical programming model and two constraint programming models for the problem. Because the problem is proven to be NP-hard, we develop two heuristic approaches, and compare their performance with methods described in the literature. Our analysis indicates that our constraint programming models perform relatively well in solution quality and execution time in small-sized problem instances. The performance of our greedy approach shows potential, reaching the optimal solution in 82.5% of instances. We also statistically demonstrate that the search algorithm enhances the quality of the solution obtained by the greedy heuristic, particularly in large sets. Hence, the solution approach, i.e., the greedy heuristic and the search algorithm proposed in this study is able to quickly reach near-optimal solutions, showing that the method is appropriate for manufacturing settings requiring sudden adjustments.

Interference-Aware and Spectral-Efficient Resource Allocation Using Complete Graphs for the D2D Communication

This paper proposes a heuristic resource block allocation using complete graphs (abbreviated to RACG) mechanism for device-to-device (D2D) communication. RACG not only can reduce the cochannel interference but also can improve the overall system capacity. For RACG, we define two pieces of user equipment in the D2D communication as one D2D pair and use the interference between each D2D pair to establish an interference graph model, which is then used to identify all complete graphs with different numbers of vertices. The innovation of RACG is right that it begins with resource block (RB) allocation for the D2D pairs in the complete graph with the largest number of vertices and considers three additional parameters to determine the allocation sequence for the D2D pairs. The three additional parameters are the frequency at which D2D pairs appear in complete graphs, the interference level of a D2D pair to other D2D pairs, and the number of RBs required. Furthermore, RACG prioritizes the reuse of allocated RB chunks during RB allocation. When a D2D pair and other D2D pairs with allocated RBs do not have mutual interference, the allocated RB chunk that has the closest number of RBs to the number of RBs required is selected and reused. We conduct performance analysis and compare the proposed RACG with a resource allocation of degree-based greedy (RADG) approach. The performance measures include the RB usage rate and occupied rate in a frame.

NoonGil Lens+: Second Level Face Recognition from Detected Objects to Decrease Computation and Performance Trade-off

Artificial intelligence has developed in various fields. The development became more significant after Neural Networks(NN) began to gain popularity. Convolutional Neural Networks(CNNs) are good at solving problems such as classification and object detection. However, the CNNs model tends to function to solve a specific problem. In the case of both object detection and face recognition it is difficult to make a single model that works well. NoonGil Lens+ is expected to be an approach that can solve both problems at once. As well as being a solution, it is also hoped that this approach can reduce the trade-off of accuracy and execution speed. The approach we propose can be called as Noongil Lens+, a system that connects YOLOv3 and FaceNet. It is inspired from a korean series called ‘STARTUP’. The author only develops the FaceNet model and the proposed system in this paper (NoonGil Lens+). Region Selection, a machine learning-based greedy approach was proposed to determine snapshots to fed into FaceNet for facial identity classification. FaceNet is trained on the CelebA dataset which has gone through the preprocessing process and is validated using the LFW dataset. NoonGil Lens+ was validated using 70 images of 7 celebrities, characters, and athletes. In general, the research was carried out successfully. NoonGil Lens+ using Region Selection has an accuracy of up to 75.2%. The Region Selection execution speed is also faster compared to Cascade Faces.

Model-based supervised sensor placement optimization to detect propellant leak in a liquid rocket engine

The sensor selection for leak detection and diagnosis in a reusable liquid rocket engine was studied. Sufficient amounts of normal and leaked training data were generated by Monte Carlo simulations considering variations in system conditions based on past firing test results. A multivariate supervised analysis, in which measurements are linearly projected onto a vector that characterizes the difference between distributions of normal and leaked cases, successfully detected the simulated leaks that could not be detected by conventional univariate red-line judgment and unsupervised principal component analysis. Significant sensors for leak detection were selected using the greedy approach based on the detection performance score. It was found that only 11 of the 27 sensors were sufficient to maintain the detection performance.

Generating Virtual Wire Sculptural Art from 3D Models

Wire sculptures are objects sculpted by the use of wires. In this article, we propose practical methods to create 3D virtual wire sculptural art from a given 3D model. In contrast, most of the previous 3D wire art results are reconstructed from input 2D wire art images. Artists usually tend to design their wire art with a single wire if possible. If not possible, they try to create it with the least number of wires. To follow this general design trend, our proposed method generates 3D virtual wire art with the minimum number of continuous wire lines. To achieve this goal, we first adopt a greedy approach to extract important edges of a given 3D model. These extracted important edges become the basis for the subsequent lines to roughly represent the shape of the input model. Then, we connect them with the minimum number of continuous wire lines by the order obtained by optimally solving a traveling salesman problem with some constraints. Finally, we smooth the obtained 3D wires to simulate the real 3D wire results by artists. In addition, we also provide a user interface to control the winding of wires by their design preference. Finally, we experimentally show our 3D virtual wire results and evaluate these created results. As a result, the proposed method is computed effectively and interactively, and results are appealing and comparable to real 3D wire art work.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds. Communicated by Ramaswamy H. Sarma

Security and Privacy in Shared HitLCPS Using a GA-Based Multiple-Threshold Sanitization Model

In Cyber-Physical Systems (CPS), especially in human-in-the-loop situations (also known as HitLCPS), the security and privacy for keeping sensitive information private is considered an emerging topic in recent decades. Many techniques in privacy-preserving data mining (PPDM) can be applied directly to HitLCPS. However, most of them to date have focused on handling singular threshold problems for data sanitization. If a sensitive itemset includes more items, it has a higher probability of being identified due to its specificity. In this work, we propose a new concept of multiple support thresholds to assist in resolving this issue. The proposed method assigns a stricter threshold for an itemset. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional Greedy PPDM approaches. The strong experimental results clearly show that our proposed method can give similar performance to conventional algorithms while still maintaining higher-levels of security and privacy protection than previous methods.

An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences.

Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.

Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply RLNN to quantum hypothesis testing and determine the optimal measurement strategy for distinguishing between multiple quantum states {ρj} while minimizing the error probability. In the case where the candidate states correspond to a quantum system with many qubit subsystems, implementing the optimal measurement on the entire system is experimentally infeasible. We use RLNN to find locally-adaptive measurement strategies that are experimentally feasible, where only one quantum subsystem is measured in each round. We provide numerical results which demonstrate that RLNN successfully finds the optimal local approach, even for candidate states up to 20 subsystems. We additionally demonstrate that the RLNN strategy meets or exceeds the success probability for a modified locally greedy approach in each random trial.While the use of RLNN is highly successful for designing adaptive local measurement strategies, in general a significant gap can exist between the success probability of the optimal locally-adaptive measurement strategy and the optimal collective measurement. We build on previous work to provide a set of necessary and sufficient conditions for collective protocols to strictly outperform locally adaptive protocols. We also provide a new example which, to our knowledge, is the simplest known state set exhibiting a significant gap between local and collective protocols. This result raises interesting new questions about the gap between theoretically optimal measurement strategies and practically implementable measurement strategies.

Generalized Empirical Regret Bounds for Control of Renewable Energy Systems in Spatiotemporally Varying Environments

Abstract This paper focuses on the empirical derivation of regret bounds for mobile systems that can optimize their locations in real-time within a spatiotemporally varying renewable energy resource. The case studies in this paper focus specifically on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In this work, we make use of a very large synthetic dataset, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches. In addition, we use dimensional analysis to generalize the aforementioned results to other spatiotemporally varying environments, making the results applicable to a wider variety of renewably powered mobile systems. Finally, to deal with more general environmental mean models, we introduce a novel approach to modify calculable regret bounds to accommodate any mean model through what we term an “effective spatial domain.”

Mutation-driven grey wolf optimizer with modified search mechanism

The Grey wolf optimizer (GWO) is a recently introduced popular swarm-intelligence-based metaheuristic algorithm, compared to other algorithms, it has shown competitive performance. Despite its popularity, the conventional GWO suffers from slow convergence rate and tendency to stuck in local optima. Therefore, there is a chance of improvement in the search mechanism of the GWO through different operators. To improve the performance of the GWO, this paper proposes a new variant of the GWO called Mutation-driven Modified Grey wolf optimizer and denoted by MDM-GWO. The MDM-GWO combines a new update search mechanism, modified control parameter, mutation-driven scheme, and greedy approach of selection in the search procedure of the GWO. The performance of the proposed MDM-GWO is evaluated on 23 well-known standard benchmark problems of wide varieties of complexities and four real-world engineering design problems. The numerical results, statistical tests, convergence, and diversity curves, and comparisons among several algorithms show the superiority of the proposed MDM-GWO.

Can greedy-like heuristics be useful for solving the Weighted Orthogonal Art Gallery Problem under regular grid discretization?

In this paper we deal with the Weighted Orthogonal Art Gallery Problem. The task is to place guards on some vertices of an orthogonal polygon P, such that the total sum of prices assigned to the chosen vertices is minimal and all points from the polygon P are covered. The problem has applications in practice, for example, in installing cameras at the corners of a building such that all interior space is covered by at least one of the cameras but the price of installation is the smallest possible. To solve the problem, the regular grid discretization of the area of polygon is applied. We propose a novel greedy approach which is based on balancing the tradeoff between the total sum of guards' costs and the total number of not yet covered points from the discretization. This new approach and an existing greedy algorithm are further hybridized with the Integer Linear programming, which is originally formulated for the wellknown Minimum Set Cover problem. Our experimental results are conducted on two sets of polygons from the literature: one with small area and the other one with large area. They proved that the proposed greedy methods can achieve the optimal solutions in most cases for the class of large-area polygons, while in case of the small area polygons, they achieve solutions of reasonable quality within lower runtime than the exact algorithms.

Provable randomized rounding for minimum-similarity diversification

When searching for information in a data collection, we are often interested not only in finding relevant items, but also in assembling a diverse set, so as to explore different concepts that are present in the data. This problem has been researched extensively. However, finding a set of items with minimal pairwise similarities can be computationally challenging, and most existing works striving for quality guarantees assume that item relatedness is measured by a distance function. Given the widespread use of similarity functions in many domains, we believe this to be an important gap in the literature. In this paper we study the problem of finding a diverse set of items, when item relatedness is measured by a similarity function. We formulate the diversification task using a flexible, broadly applicable minimization objective, consisting of the sum of pairwise similarities of the selected items and a relevance penalty term. To find good solutions we adopt a randomized rounding strategy, which is challenging to analyze because of the cardinality constraint present in our formulation. Even though this obstacle can be overcome using dependent rounding, we show that it is possible to obtain provably good solutions using an independent approach, which is faster, simpler to implement and completely parallelizable. Our analysis relies on a novel bound for the ratio of Poisson-Binomial densities, which is of independent interest and has potential implications for other combinatorial-optimization problems. We leverage this result to design an efficient randomized algorithm that provides a lower-order additive approximation guarantee. We validate our method using several benchmark datasets, and show that it consistently outperforms the greedy approaches that are commonly used in the literature.

A Comparison of Greedy Algorithm and Dynamic Programming Algorithm

Two algorithms to handle the problem include greedy algorithms and dynamic programming. Because of their simplicity, intuitiveness, and great efficiency in addressing problems, they are frequently employed in a variety of circumstances. The connection and difference of the two algorithms are compared by introducing the essential ideas of the two algorithms. The knapsack problem is a classic problem in computer science. In the application of solving the backpack problem, greedy algorithm is faster, but the resulting solution is not always optimal; dynamic programming results in an optimal solution, but the solving speed is slower. The research compares the application properties and application scope of the two strategies, with the greedy approach being the better approach in solving the knapsack problem.

Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks

For efficient and robust information exchange in the vehicular ad-hoc network, a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel. In addition, we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards. Unfortunately, there may be some misbehaving nodes and due to their selfish and greedy approach, these nodes may not help others on the network. To deal with this challenge, trust-based misbehavior avoidance schemes are generally reflected as the capable resolution. In this paper, we employed a fair incentive mechanism for cooperation aware vehicular communication systems. In order to deploy a comprehensive credit based rewarding scheme, the proposed reward-based scheme fully depends on secure and reliable cryptographic procedures. In order to achieve the security goals, we used the cryptographic scheme to generate a certified public key for the authenticity of every message exchange over the network. We evaluated the friction of misbehaving vehicles and the effect of rewarding schemes in context with honest messages dissemination over the network.

Error analysis of a model order reduction framework for financial risk analysis

A parametric model order reduction (MOR) approach for simulating high-dimensional models arising in financial risk analysis is proposed on the basis of the proper orthogonal decomposition (POD) approach to generate small model approximations for high-dimensional parametric convection-diffusion reaction partial differential equations (PDE). The proposed technique uses an adaptive greedy sampling approach based on surrogate modeling to efficiently locate the most relevant training parameters, thus generating the optimal reduced basis. The best suitable reduced model is procured such that the total error is less than a user-defined tolerance. The three major errors considered are the discretization error associated with the full model obtained by discretizing the PDE, the model order reduction error, and the parameter sampling error. The developed technique is analyzed, implemented, and tested on industrial data of a puttable steepener under the two-factor Hull-White model. The results illustrate that the reduced model provides a significant speedup with excellent accuracy over a full model approach, demonstrating its potential for applications to the historical or Monte Carlo Value-at-Risk calculations.