Search Region Research Articles

Multi-Neighborhood Simulated Annealing-Based Iterated Local Search for Colored Traveling Salesman Problems

A coloring traveling salesman problem (CTSP) generalizes the well-known multiple traveling salesman problem, where colors are used to differentiate salesmen’s the accessibility to individual cities to be visited. As a useful model for a variety of complex scheduling problems, CTSP is computationally challenging. In this paper, we propose a Multi-neighborhood Simulated Annealing-based Iterated Local Search (MSAILS) to solve it. Starting from an initial solution, it iterates through three sequential search procedures: a multi-neighborhood simulated annealing search to find a local optimum, a local search-enhanced edge assembly crossover to find nearby high-quality solutions around a local optimum, and a solution reconstruction procedure to move away from the current search region. Experimental results on two groups of 45 medium and large benchmark instances show that it significantly outperforms state-of-the-art algorithms. In particular, it is able to discover new upper bounds for 29 instances while matching 8 previous best-known upper bounds. Hence, this work greatly advances the field of CTSP.

Evaluation of Irrigation Water Quality Index (IWQI) for Habbaniya Lake in Anbar Governorate, Iraq

A study was conducted to estimate the Irrigation Water Quality Index (IWQI) for Habbaniya Lake in central Iraq for irrigation purposes. The samples were taken at sixteen stations in October 2019. Studied samples were examined for ten Physico-chemical parameters Hydrogen Number (pH), Electrical Conductivity (EC), Temperature (T °C), Total Dissolved Solids (TDS), Total Hardness (T.H.), Turbidity (NTU), Sodium Absorption Ratio (SAR), Na+, Cl−, and HCO3 −. The results showed that 68.75% of the study water samples fall within the low restriction category (LR) irrigation water in eleven stations (which is dominant in all parts) and 25% of the search region falls under the no restriction category (NR) irrigation water in four stations (which is dominant in the northwestern part at station 5-8). Meanwhile, a small part of the study area represents 6.25% of the water samples is classified as a moderate restriction (MR) irrigation water in one station (in the central part). Accordingly, these categories are suitable to use only with the soil having low to moderate permeability with a plant of some kind for specified tolerance of moderate salts content. The IWQI is homogeneous and suitable for irrigation in all parts, especially the northwestern part which represents the entrance waters through Warar Canal that fed principally from the Euphrates River.

University Examination Timetabling Using a Hybrid Black Hole Algorithm

University timetabling construction is a complicated task that is encountered by universities in the world. In this study, a hybrid approach has been developed to produce timetable solution for the university examination timetabling problem. Black Hole Algorithm (BHA), a population-based approach that mimics the black hole phenomenon has been introduced in the literature recently and successfully applied in addressing various optimization problems. Although its effectiveness has been proven, there still exists inefficiency regarding the exploitation ability where BHA is poor in fine tuning search region in reaching for good quality of solution. Hence, a hybrid framework for university examination timetabling problem that is based on BHA and Hill Climbing local search is proposed (hybrid BHA). The aim of this hybridization is to improve the exploitation ability of BHA in fine tuning the promising search regions and convergence speed of the search process. A real-world university examination benchmark dataset has been used to evaluate the performance of hybrid BHA. The computational results demonstrate that hybrid BHA capable of generating competitive results and recording best results for three instances, compared to the reference approaches and current best-known recorded in the literature. Other than that, findings from the Friedman tests show that the hybrid BHA ranked second and third in comparison with hybrid and meta-heuristic approaches (total of 27 approaches) reported in the literature, respectively.

Broad search for gravitational waves from subsolar-mass binaries through LIGO and Virgo’s third observing run

We present a search for gravitational waves from the coalescence of binaries which contain at least one subsolar-mass component using data from the LIGO and Virgo observatories through the completion of their third observing run. The observation of a merger with a component below $1\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ would be a clear sign of either new physics or the existence of a primordial black hole population; these black holes could also contribute to the dark matter distribution. Our search targets binaries where the primary has mass ${M}_{1}$ between 0.1 and $100\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ and the secondary has mass ${M}_{2}$ from 0.1 to $1\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ for ${M}_{1}<20\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ and 0.01 to $1\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ for ${M}_{1}\ensuremath{\ge}20\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$. Sources with ${M}_{1}<7\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$, ${M}_{2}>0.5\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ are also allowed to have orbital eccentricity up to ${e}_{10}\ensuremath{\sim}0.3$. This search region covers from comparable to extreme mass ratio sources up to ${10}^{4}:1$. We find no statistically convincing candidates and so place new upper limits on the rate of mergers; our analysis sets the first limits for most subsolar sources with $7{M}_{\ensuremath{\bigodot}}<{M}_{1}<20\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ and tightens limits by $\ensuremath{\sim}8\ifmmode\times\else\texttimes\fi{}(1.6\ifmmode\times\else\texttimes\fi{})$ where ${M}_{1}>20\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ (${M}_{1}<7\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$). Using these limits, we constrain the dark matter fraction to below $0.3(0.7)%$ for 1 $(0.5)\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ black holes assuming a monochromatic mass function. Due to the high merger rate of primordial black holes beyond the individual source horizon distance, we also use the lack of an observed stochastic background as a complementary probe to limit the dark matter fraction. We find that although the limits are, in general, weaker than those from the direct search, they become comparable at $0.1\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$.

An approach to enhance performance of age invariant face recognition

Age invariant face recognition (AIFR) is currently a study topic with several potential uses. It offers a variety of real-world applications, including passport renewal, driver’s license renewal, locating missing children, locating criminals, providing security to VIPs etc. In the field of AIFR, scientific efforts have increased. Matching faces of big age differences is thus a challenge, owing to the significant variation in appearance between young and elderly age. The appearance and form of the face deteriorate with age, making facial recognition the most difficult task. AIFR has become a highly common and difficult chore in recent years. In this discipline, the set of feature extraction and classification algorithms is crucial. This paper addresses the issues raised above by proposing an enhanced ASM approach for extracting features from 2D search regions using handcrafted and deep image features in conjunction with a 7-layer CNN architecture and a smaller image size of 32x32 pixels to reduce delay time and space complexity. Using the standard dataset LAG, the study approach entails running many tests to evaluate the proposed system’s performance. The results show that the suggested method beats state-of-the-art algorithms in face recognition and achieves good accuracy throughout the age spectrum. The presented methodology achieves a maximum accuracy of 91.76 percent for the LAG database, outperforming all existing state-of-the-art methodologies.

Adaptive dynamic surrogate-assisted evolutionary computation for high-fidelity optimization in engineering

Surrogate-assisted evolutionary computation have received much more attention in the field of optimization because of its ability to reduce effectively the CPU cost. However, as the dimension of the search space increases, the number of samples required to construct the global accurate surrogate model will increase exponentially. In order to improve the computational efficiency for achieving high-precision optimization in high-dimensional search space, an adaptive dynamic surrogate-assisted evolutionary computation approach based on variable search region is presented in this paper. The basic idea of the present method is to abandon the high accurate approximation of surrogate model on the global search space which requires a large number of samples, but focus on the search of the smaller local region where the optimal solution is located. Then refine the samples to construct a higher-precision local surrogate model on the smaller local search region, which moves with the movement of the current optimal solution. Numerical experiments show that the highly accurate optimal solution can be obtained by less than five times of adaptation. It is also applied successfully to the aerodynamic shape design optimization of transonic airfoil and wing, and the results show that present adaptive approach greatly improves the computational efficiency by about ten times compared with the traditional static global approximation surrogate model.

Turkey’s Expansion In Eastern Africa: The Case Of Somalia

The paper analyzes Turkish involvement in Eastern Africa by the case of Turkey-Somalia relations. The article represents how Turkey successfully went from providing humanitarian aid to taking control over the important infrastructures and locating own military base in Somalia. The research shows that the Turkish involvement in Somalia is mainly benefiting Turkey rather than Somalia, which does not possess enough resources to speak on equal terms with Turkey. The conclusions drawn from the research are the following: 1) Africa’s role as a political theatre is gradually increasing, as more and more players are getting involved, 2) Turkey is getting actively engaged in Africa on its way to becoming a regional power, 3) The case of Turkish involvement in Somalia represents a “Turkish model” that can be offered to struggling states of Africa, i.e. large economic and political involvement in exchange for communication and military infrastructure 4) Somalia’s case can become a motivation for other unstable countries in the region in search for a patron state.

Multi‐objective memetic approach for the optimal web services composition

AbstractService composition is the process of combining a set of elementary or atomic services. The aim is to produce a new composite service to satisfy the user's request that cannot be satisfied by the atomic services. Combining multiple services is a complex problem that has been the subject of several research studies. The meta‐heuristic approaches are good techniques that have been used to solve several complex problems in various domains. These techniques are able to discover promising search regions and locate good quality solutions in reasonable time without exploring the whole solution space. In this paper, we deal with the problem of optimal web service composition by using meta‐heuristic approaches. Given a set of services and a set of tasks to be completed, the problem is to find the best set of services composition to complete all tasks where each service must be assigned to a given task. This problem can be modelled as a combinatorial optimization problem with a set of objective functions that need to be optimized. We search for a composite service that allows us to execute the considered tasks and offers the best quality of services (QoS). More precisely, we search for an execution plan that indicates for each task the assigned service. First, we propose a multi‐objective local search based meta‐heuristic (MO‐LS) and a multi‐objective genetic algorithm (MO‐GA) to handle our problem. Then we propose a multi‐objective memetic algorithm (MO‐MA) that combines the two methods LS and GA. The role of GA is to detect promising regions to be explored. The role of LS is to exploit efficiently the potential regions created by GA. Four objective functions are used to compute the Pareto optimal set of solutions. The main objective is to minimize cost and time and to maximize availability and reputation and produce a good composite service. The three proposed approaches namely MO‐LS, MO‐GA, and MO‐MA are evaluated on some datasets generated randomly and on the well‐known QWS dataset to select the best fit services in terms of maximum or minimum aggregated end‐to‐end QoS parameters. The numerical results are encouraging and demonstrate the effectiveness of the proposed MO‐MA for the web service composition.

Non-Slicing Very Large Scale Integration Floorplan Optimization Technique Using an Improved Modified Memetic Algorithm with O-Tree Representation

Interconnects in the VLSI floorplan play an essential role in determining a particular chip’s speed and parametric losses. For researchers, balancing between optimization of area and wirelength has been the central focus. This paper has proposed an improved memetic algorithm according to the floorplan problem in which the Genetic Algorithm is used for global exploration and an inherent efficient local search method for improving the result in the search region. The representation used for the floorplan is O-Tree (Ordered Tree) implemented through the Code-Based Location Search and Position (CBLSP) methodology. For wirelength calculation, the Half Perimeter Wire Length (HPWL) methodology is utilized. The mentioned techniques are checked on MCNC benchmark circuits with different preferences for wirelength and area. It has been found that the proposed methodology has generated the best and near to best results for area. In terms of wirelength, the proposed methodology has shown improvement with a range of 3.97%–65.47%. For multi-objective optimization with the area and wirelength (α = 0.5, β = 0.5), significant improvement in wirelength with a 25.1% decrease for the APTE benchmark, 47.4% decrease for HP Benchmark, and 10.7% decrease for the AMI33 benchmark circuit has been recorded.

A Robust Strategy for Large-Size Optical and SAR Image Registration

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.

Reduction as an improvement of a precise satellite positioning based on an ambiguity function

Abstract It is well-known that the solution domain has a discrete character in precise satellite positioning because of the integer nature of ambiguities. Therefore, in addition to the classic least squares estimation, the search procedure has to be employed in the computation process to obtain the so-called ‘fixed solution.’ The article’s subject is to improve the search procedure conducted in the coordinate domain. The reduction process is to transform the original math model into an equivalent one in the sense of obtaining the same solution. The reduction aims to increase the efficiency of searching for some parameters, i. e., integer ambiguities. The article presents the concept of employing the reduction procedure to the computation process of precise positioning based on the ambiguity function. The transformation matrix for the reduction is based on the well-known integer decorrelation procedure. Numerical experiment results display a positive impact of the reduction process on the search procedure efficiency. This positive impact is manifested by a dramatic decrease in the number of candidates needed to test all admissible solutions inside the search region. The percentage decrease in that magnitude is at least 50 % for all session lengths and achieves a maximum value of over 75 % for the 10-minute session. Computational time decreases by over 40 % while short sessions are processed. There is no improvement for sessions longer than 15 minutes, but, as explained in the paper, there is no need to improve that magnitude in such cases.

MICQ-IPSO: An effective two-stage hybrid feature selection algorithm for high-dimensional data

In machine learning and pattern recognition tasks, classification performance is often degraded due to the existence of irrelevant and redundant features, especially for high-dimensional data. As a data preprocessing tool, feature selection can improve classification performance while reducing the number of features. Focusing on high-dimensional data, we propose a novel two-stage hybrid feature selection method that combines the maximum information coefficient (MIC) based Q-learning algorithm and the improved particle swarm optimization (PSO) based algorithm, named as MICQ-IPSO. In the first stage, we employed an intelligent feature pre-screening operation to get a rough feature subset, which introduced MIC value as the correlation measure and automated the determination of the screening threshold by Q-learning. In the second stage, we applied an improved PSO-based method to get an optimal feature subset. During this stage, a swarm initialization strategy based on MIC correlation was used to narrow the search range and accelerate swarm convergence. To further enhance the exploitability, a deeper local search operation was performed in the search region. Moreover, a particle reset strategy was adopted to help particles jump out of the local optimal solution. Finally, we evaluated our algorithm against several state-of-the-art feature selection approaches on 17 benchmark datasets. The experimental results demonstrate the effectiveness and competitiveness of the proposed algorithm.

A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm

The maximum power point tracking (MPPT) controller can monitor the voltage and current of photovoltaic (PV) array in real-time to acquire the maximum power output for battery charging in PV system. In order to improve the accuracy and rapidity of tracking process, a bionic two-stage MPPT control strategy composed of fast positioning stage and precise determination stage is proposed in this paper for optimizing the duty cycle d of DC–DC converter. Firstly, an improved artificial bee colony algorithm with simplified probability selection mechanism and novel employed bee phase is presented to balances exploration and exploitation for excellent rapidity in positioning rough search region around global peak. Then, the simultaneous heat transfer search (SHTS) algorithm is adopted for accurately acquiring the global maximum power point in obtained search region. On the one hand, the SHTS can reduce the subjectivity in artificial setting of the parameters to ensure the universality and accuracy of search process. On the other hand, the parallel search in SHTS can decrease the optimization time effectively. Finally, extensive simulation results illustrate that this two-stage MPPT strategy shows excellent performance in precisely identifying the GMPP of PV system with phenomenal rapidity among multiple peaks. Moreover, it outperforms all the counterparts in tracking speed and accuracy under partial shading conditions.

Search for neutral long-lived particles in pp collisions at sqrt{s} = 13 TeV that decay into displaced hadronic jets in the ATLAS calorimeter

A search for decays of pair-produced neutral long-lived particles (LLPs) is presented using 139 fb−1 of proton-proton collision data collected by the ATLAS detector at the LHC in 2015–2018 at a centre-of-mass energy of 13 TeV. Dedicated techniques were developed for the reconstruction of displaced jets produced by LLPs decaying hadronically in the ATLAS hadronic calorimeter. Two search regions are defined for different LLP kinematic regimes. The observed numbers of events are consistent with the expected background, and limits for several benchmark signals are determined. For a SM Higgs boson with a mass of 125 GeV, branching ratios above 10% are excluded at 95% confidence level for values of c times LLP mean proper lifetime in the range between 20 mm and 10 m depending on the model. Upper limits are also set on the cross-section times branching ratio for scalars with a mass of 60 GeV and for masses between 200 GeV and 1 TeV.

Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling

This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The results are compared with advanced well-known metaheuristic approaches. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption.

Robot Path Planning via Neural-Network-Driven Prediction

This article presents a novel heuristic method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neural-network-driven prediction (NEED)</i> for robot path planning problems. Different from classical heuristic methods, NEED is not designed for some specific environments, which can be applied to various environments. NEED has an encoder–decoder structure, which consists of three modules: convolutional neural network backbone, spatial pooling module, and decoder module. By learning from a number of successful path planning cases, NEED can learn to analyze the environment structure and predict the promising search region for the new path planning problem. This predicted region serves as a heuristic to guide the search direction of path planning algorithms. A series of simulation experiments are conducted to demonstrate that NEED significantly improves the algorithm performance on solving new path planning problems. Meanwhile, NEED can also be applied to highly dynamic environments since it can output the prediction results at a speed of over 100 Hz on a laptop.

In silico Structural and Functional Characterization of a Hypothetical Protein from Stenotrophomonas maltophilia SRM01

Stenotrophomonas maltophilia is a low-virulence opportunistic pathogen that causes human infections, especially in profound ill patients. Even if the bacterial genomes seem understood, the activities of many proteins are unknown. The purpose of our current research is to unravel the functional characteristics i.e. functional domain search and valuable regions of a hypothetical protein that would aid in the identification of potential drug targets in Stenotrophomonas maltophilia. The hypothetical protein of S.maltophilia was located and annotated using different in silico techniques. Our target protein was predicted to be Transcrip Reg superfamily YebC/PmpR based on motif and domain analysis by functional annotation tools. The regulator proteins of the YebC family are part of a vast collection of widely conserved hypothetical proteins with unclear functions. Examining and reviewing the function of YebC family protein, they repress Quorum sensing by directly binding to the promoter region of QS master regulator pqrS. It has also been reported that T3SS expression is regulated by YebC, to activate the virulence expression direct interaction with one of the T3SS promoters is needed.

JOINT BUNDLE ADJUSTMENT OF THERMAL INFRA-RED AND OPTICAL IMAGES BASED ON MULTIMODAL MATCHING

Abstract. Despite the fact that Thermal Infrared (TIR) cameras have been in use for decades, processing of TIR images poses a variety of challenges when compared to optical images, which are captured in the visible part of the electromagnetic spectrum. The estimation of the exterior orientation of TIR cameras by bundle adjustment is a difficult task due to the limited geometric resolution of a TIR camera and the low image quality in terms of contrast and texture compared to optical images. Optical images have a potential to increase TIR external orientation accuracy by incorporating them into a joint bundle adjustment. However, because the modality gap between those two image types is large, classical point matching algorithms typically fail to find matches, making processing both image types in the joint bundle challenging. In order to locate matching points in both modalities, this study suggests using the Edge Histogram Descriptor (EHD) in the frequency domain representation of the images based on phase congruency. To properly allocate edges from the phase congruency, which are then employed in EHD, non-maximum suppression and hysteresis thresholding are used. Considering that both sensors are fixed rigidly to a single platform, the search region for the matching point candidate of the TIR image is determined based on stereo calibration of a thermal/optical stereo setup combined with geometric constraints. The final matching is based on the cosine distance, while RANdom SAmple Consensus (RANSAC) is used in order to eliminate outliers. The findings of this study show that using a joint bundle adjustment with optical images versus a bundle adjustment only with TIR images improves TIR image orientation, which is supported by the increased accuracy of the adjusted Ground Control Point (GCP) coordinates.

Republic of Singapore Air Force Helicopter Search-and-Rescue and Medical Evacuations: A 5-Year Review

The Republic of Singapore Air Force (RSAF) provides Helicopter Search-and-Rescue (SAR) and Helicopter Medical Evacuation (Heli-Medevac) coverage for the Singapore Aeronautical Search and Rescue Region (ASSR) in the South China Sea, spanning 840,000 km2. This region contains busy international shipping lanes and air traffic routes. Each year, Singapore's Helicopter SAR and Heli-Medevac service is activated multiple times to rescue personnel lost at sea or to evacuate ill and injured ship sailors or passengers to tertiary hospitals in Singapore for stabilization and advanced care. This is a retrospective review on all civilian SAR and Heli-medevac activations by the RSAF over a 5-year period from 2016 to 2020. Case profiles, presenting conditions, in-flight treatment, and patient outcomes are reviewed and discussed. Key operational observations made from RSAF's SAR and Heli-Medevac, as well as lessons learned from these missions, are discussed in this article.

Robust visual tracking algorithm with coattention guided Siamese network

In recent years, Siamese trackers locate the target based on the cross-correlation results of the template and the search branch. Siamese trackers emphasized better features embedding and similarity matching. The latest work focuses on enriching the feature representation of the template. However, it is still difficult for the tracker to eliminate the side effect of the distractors in the search region, resulting in the decline in tracking accuracy. We propose a coattention guided Siamese network (CGS) for visual tracking from two aspects. To mine the inherent correlation between the template and search branch, a coattention block is proposed that can guide the target-aware feature learning during the tracking process. To wisely choose the target-specific features, a self-gated mechanism is exploited on the template features, which can adaptively reallocate the confidence of channels and strengthen the feature representation. We conduct extensive experiments on OTB2015, VOT2016, and GOT-10K benchmarks to evaluate the performance of the proposed method. Experimental results demonstrate that our CGS network performs favorable against other state-of-the-art methods. Moreover, our CGS runs at 79FPS, far above the real-time requirement.