Two-phase Search Research Articles

Accelerating Two-Phase Multiobjective Evolutionary Algorithm for Electric Location-Routing Problems

Electric location-routing problem is a challenging problem consisting of the optimization of electric vehicle routing and charging facility location, simultaneously. Existing algorithms generally adopt the two-phase search strategy to alternately optimize the routing and the location. However, they are usually criticized for the inefficiency as the problem scale increases. In order to improve the search efficiency in each phase, we propose an accelerating two-phase multiobjective evolutionary algorithm, where the learning method is used to mine the useful information from the historical search process to generate the high-quality routing and location offspring. To be specific, in the routing optimization phase, an interpolation method is developed to extract the frequent visiting orders existing in the historical best solutions. These frequent visiting orders are used to create potential routing offspring that can accelerate the convergence toward the optimal solutions. In the location optimization phase, a surrogate model is used to approximatively represent the relationship from routing to location, which can directly output a promising location scheme for a given routing offspring and thus reduce the optimization time. Experimental results on different scales of test instances demonstrate the competitiveness of the proposed algorithm in comparison with several state of-the-art algorithms, including four widely used heuristic algorithms and two multiobjective evolutionary algorithms.

Perturbation-Based Thresholding Search for Packing Equal Circles and Spheres

This paper presents an effective perturbation-based thresholding search for two popular and challenging packing problems with minimal containers: packing N identical circles in a square and packing N identical spheres in a cube. Following the penalty function approach, we handle these constrained optimization problems by solving a series of unconstrained optimization subproblems with fixed containers. The proposed algorithm relies on a two-phase search strategy that combines a thresholding search method reinforced by two general-purpose perturbation operators and a container adjustment method. The performance of the algorithm is assessed relative to a large number of benchmark instances widely studied in the literature. Computational results show a high performance of the algorithm on both problems compared with the state-of-the-art results. For circle packing, the algorithm improves 156 best-known results (new upper bounds) in the range of [Formula: see text] and matches 242 other best-known results. For sphere packing, the algorithm improves 66 best-known results in the range of [Formula: see text], whereas matching the best-known results for 124 other instances. Experimental analyses are conducted to shed light on the main search ingredients of the proposed algorithm consisting of the two-phase search strategy, the mixed perturbation and the parameters. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 61703213 and 61933005]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1290 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0004 ) at ( http://dx.doi.org/10.5281/zenodo.7579558 ).

Environmentally Responsive Materials for Building Envelopes: A Review on Manufacturing and Biomimicry-Based Approaches.

Buildings must adapt and respond dynamically to their environment to reduce their energy loads and mitigate environmental impacts. Several approaches have addressed responsive behavior in buildings, such as adaptive and biomimetic envelopes. However, biomimetic approaches lack sustainability consideration, as conducted in biomimicry approaches. This study provides a comprehensive review of biomimicry approaches to develop responsive envelopes, aiming to understand the connection between material selection and manufacturing. This review of the last five years of building construction and architecture-related studies consisted of a two-phase search query, including keywords that answered three research questions relating to the biomimicry and biomimetic-based building envelopes and their materials and manufacturing and excluding other non-related industrial sectors. The first phase focused on understanding biomimicry approaches implemented in building envelopes by reviewing the mechanisms, species, functions, strategies, materials, and morphology. The second concerned the case studies relating to biomimicry approaches and envelopes. Results highlighted that most of the existing responsive envelope characteristics are achievable with complex materials requiring manufacturing processes with no environmentally friendly techniques. Additive and controlled subtractive manufacturing processes may improve sustainability, but there is still some challenge to developing materials that fully adapt to large-scale and sustainability needs, leaving a significant gap in this field.

Real-time surrogate-assisted preprocessing of streaming sensor data

A new dynamic programming based algorithm for real-time simultaneous segmentation, compression, and C2-smoothing of potentially infinite data streams – studied in the context of streaming sensor data – is shown to be both effective and (energy) efficient. Its three key elements – the cubic splinelet based adaptive search space, the objective function (combining two measures of approximation errors), and the two-phase search space reduction technique (combining segment length based pruning and surrogate based pruning) – are presented in detail. The output quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show that the new algorithm outperforms both reference algorithms for all test streams, in some cases—significantly (up to 32% smaller approximation errors and up to 41% higher compression ratios). Due to the effective search space auto-adaptation and special pruning techniques, in typical cases, this is obtained with significantly lower computational cost. The proposed algorithm can be applied to various domains including online compression and smoothing of streaming data coming from IoT devices, sensor networks, and sensors located in autonomous vehicles (cars, drones) and robots. The possible application areas also include real-time IoT analytics and embedded time-series databases.

Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites

This paper deals with the problem of coordinating a truck and multiple heterogeneous unmanned aerial vehicles (UAVs or drones) for last-mile package deliveries. Existing literature on truck–drone tandems predominantly restricts the UAV launch and recovery operations (LARO) to customer locations. Such a constrained setting may not be able to fully exploit the capability of drones. Moreover, this assumption may not accurately reflect the actual delivery operations. In this research, we address these gaps and introduce a new variant of truck–drone tandem that allows the truck to stop at non-customer locations (referred to as flexible sites) for drone LARO. The proposed variant also accounts for three key decisions — (i) assignment of each customer location to a vehicle, (ii) routing of truck and UAVs, and (iii) scheduling drone LARO and truck operator activities at each stop, which are always not simultaneously considered in the literature. A mixed integer linear programming model is formulated to jointly optimize the three decisions with the objective of minimizing the delivery completion time (or makespan). To handle large problem instances, we develop an optimization-enabled two-phase search algorithm by hybridizing simulated annealing and variable neighborhood search. Numerical analysis demonstrates substantial improvement in delivery efficiency of using flexible sites for LARO as opposed to the existing approach of restricting truck stop locations. Finally, several insights on drone utilization and flexible site selection are provided based on our findings.

Towards Secure and Efficient Equality Conjunction Search Over Outsourced Databases

Searchable symmetric encryption enables a cloud server to answer queries directly over encrypted data. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading false positives and requiring the local search at the client side. In this article, we propose a novel approach to meet these requirements while minimizing the clients work and communication cost. First, a relaxed notion of ciphertext indistinguishability on partitioned data is formalized, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the server to perform search of relevant data and filter false positives. We present a construction for achieving these goals through a two-phase search algorithm. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. The experiment results on large real-world data-sets show that our approach outperforms the state-of-the-art. This article focuses on the class of equality conjunction search, but it applies to the general class of Boolean queries of equalities because the latter can be reduced to several equality conjunction queries.

A Tandem Running Strategy-Based Heat Transfer Search Algorithm and Its Application to Chemical Constrained Process Optimization

Constrained optimization problems (COPs) are widely encountered in chemical engineering processes, and are normally defined by complex objective functions with a large number of constraints. Classical optimization methods often fail to solve such problems. In this paper, to solve COPs efficiently, a two-phase search method based on a heat transfer search (HTS) algorithm and a tandem running (TR) strategy is proposed. The main framework of the MHTS–TR method aims to alternate between a feasible search phase that only examines feasible solutions, using the HTS algorithm, and an infeasible search phase where the treatment of infeasible solutions is relaxed in a controlled manner, using the TR strategy. These two phases play different roles in the search process; the former ensures an intensified optimum in a relevant feasible region, whereas the latter is used to introduce more diversity into the former. Thus, the ensemble of these two complementary phases can provide an effective method to solve a wide variety of COPs. The proposed variant was investigated over 24 well-known constrained benchmark functions, and then compared with various well-established metaheuristic approaches. Furthermore, it was applied for solving a chemical COP. The promising results demonstrate that the MHTS–TR approach is applicable for solving real-world COPs.

SecVKQ: Secure and verifiable kNN queries in sensor–cloud systems

Sensor–cloud has gained increasingly popularity since it bridges the physical world and the cyber world through pervasive computation. This paper focuses on secure and verifiable k nearest neighbor (kNN) queries over large-scale outsourced datasets in sensor–cloud systems. Existing work in this line often incurred high computational and communication overheads, remaining far away from practical and scalable. To this end, we propose SecVKQ, a two-phase search framework, which mainly includes a preliminary screening phase and an exact search phase. SecVKQ purposely takes advantage of secure data separation and adaptive encryption strategy, embracing edge servers into the classic dual-cloud model, so as to optimize query performance. Under SecVKQ, we design a series of secure protocols and develop a succinct verification strategy to derive a unified solution. The experimental results demonstrate the effectiveness of SecVKQ. Compared to the state-of-the-art work, SecVKQ achieves a speed-up of two orders of magnitude in search latency, and a savings of 50% communication cost for verification.

Efficient automatic scheduling of imaging and vision pipelines for the GPU

We present a new algorithm to quickly generate high-performance GPU implementations of complex imaging and vision pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels. We address the scalability challenge of extending search-based automatic scheduling to map large real-world programs to the deep hierarchies of memory and parallelism on GPU architectures in reasonable compile time. We achieve this using (1) a two-phase search algorithm that first ‘freezes’ decisions for the lowest cost sections of a program, allowing relatively more time to be spent on the important stages, (2) a hierarchical sampling strategy that groups schedules based on their structural similarity, then samples representatives to be evaluated, allowing us to explore a large space with few samples, and (3) memoization of repeated partial schedules, amortizing their cost over all their occurrences. We guide the process with an efficient cost model combining machine learning, program analysis, and GPU architecture knowledge. We evaluate our method’s performance on a diverse suite of real-world imaging and vision pipelines. Our scalability optimizations lead to average compile time speedups of 49x (up to 530x). We find schedules that are on average 1.7x faster than existing automatic solutions (up to 5x), and competitive with what the best human experts were able to achieve in an active effort to beat our automatic results.

Moving Agents in Formation in Congested Environments

In this paper, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation. Previous work includes controller-based algorithms, swarm-based algorithms, and potential-field-based algorithms. They usually focus on only one or the other of these tasks, solve the problem greedily without systematic search, and thus generate costly solutions or even fail to find solutions in congested environment. In this paper, we develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it. It also identifies the critical segments of the leader's path where the other agents cannot preserve the desired formation and the refinement of which has thus to be delegated to the second phase. In the second phase, SWARM-MAPF refines these segments. Theoretically, we prove that SWARM-MAPF is complete. Empirically, we show that SWARM-MAPF scales well and is able to find close-to-optimal solutions.

Promising Aging in Community Models in the U.S.: Village, Naturally Occurring Retirement Community (NORC), Cohousing, and University-Based Retirement Community (UBRC).

Aging in Community (AIC) is the preferred way to age. This systematic review identified promising AIC models in the U.S. and analyzed model characteristics and push-pull factors from older adults’ perspectives. Push factors are those driving older adults to leave, while pull factors attract them to stay in a community. We conducted a two-phase search strategy using eight databases. Phase I identified promising AIC models and Phase II expanded each specific model identified. Fifty-two of 244 screened articles met the criteria and were analyzed. We identified four promising AIC models with the potential to achieve person-environment (P-E) fit, including village, naturally occurring retirement community (NORC), cohousing, and university-based retirement community (UBRC). Each has a unique way of helping older adults with their aging needs. Similar and unique push-pull factors of each AIC model were discussed. Analyses showed that pull factors were mostly program factors while push factors were often individual circumstances. Continued research is needed to address the challenges of recruiting minority older adults and those of lower socio-economic status, meeting older adults’ diverse and dynamic needs, and conducting comparative studies to share lessons learned across the globe.

How T Cells Do the “Search for the Needle in the Haystack”

In the body, a T cell is confronted with millions of antigen-presenting cells (APCs) in the search for potentially harmful antigen. To elicit an appropriate immune response, this search has to be performed as fast and as precise as possible. These two requirements, however, are at odds with each other: fast searches lack accuracy, whereas high fidelity decisions are typically time-consuming. Here, we use the archetypical search for the needle in the haystack as an analogy for the T cell’s search problem. We provide a statistical framework to quantitatively estimate the constraints of search strategies for rare instances. Particularly, we propose a solution for balancing the demand for high speed with low error rates. It takes advantage of a two-phase search process, which combines a first rapid scan with a second high-fidelity check. Finally, we provide arguments that support a two-phase search model for identification of antigen-positive APCs by T cells.

Privacy-Protected Route-Based Spatial-Textual Location Search in Road Networks

Location search and recommendation have received significant attention in recent years. To protect the users’ privacy, we propose and study a novel privacy-protected route-based spatial-textual location (PPRSTL) query in road networks. Given a set of locations $O$ with the textual description, a query route $q$ with a set of query keywords to describe user’s preference, the PPRSTL query finds the location with the highest spatial-textual similarity to the query route $q$ . We believe that such type of query is very useful in many mobile applications such as location recommendation and discovery, and location-based services in general. The problem is challenging due to three reasons: (1) how to model the spatial and textual similarity practically; (2) how to constrain the search space in a comparatively small range in the spatial and textual domains, and; (3) how to protect the users’ privacy when returning the query results. To overcome these challenges, we define a boolean spatial-textual measure to evaluate the similarity in the spatial and textual domains, and a two-phase search mechanism to protect users’ privacy. We develop two expansion search algorithms that follow the filter-and-refinement paradigm, to compute the queries efficiently. Two pairs of upper and lower bounds are defined to prune the search space effectively. In addition, we adopt an expansion center selection method to further enhance the query efficiency. Finally, we conduct extensive experiments on real and synthetic spatial data sets to verify the performance of the developed algorithms.

Measurement of Physical Activity in Adults with Muscular Dystrophy: A Systematic Review

There is little consensus about measurement of physical activity in adults with muscular dystrophy. This systematic review summarizes evidence for measurement properties of direct and indirect measures of physical activity in adults with muscular dystrophy. A two-phase search for peer-reviewed articles identified firstly, studies which measured physical activity in this population and secondly, studies reporting the measurement properties of activity measures. Methodological quality was assessed using COSMIN guidelines and a best evidence synthesis conducted. Phase 1 included 53 studies identifying 63 measures including accelerometers, direct observation, heartrate monitors, calorimetry, positional sensors, activity diaries, single scales and questionnaires. Phase 2 included 26 studies of measurement properties for 32 measures. Methodological quality of the included studies was low, only 2 were rated good. There was insufficient evidence to robustly recommend any physical activity measures and further research is required to validate measures of physical activity for adults with muscular dystrophy. Based on the findings of this review, measures with potential for further study have been highlighted.

Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy.

Here, a two-phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi-objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache-based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.

Evidence of the factors that influence the utilisation of Kangaroo Mother Care by parents with low-birth-weight infants in low- and middle-income countries (LMICs): a scoping review protocol

BackgroundThe Sustainable Development Goal (SDG) 3 emphasises on reducing neonatal deaths caused by low birth weight (LBW) complications by the implementation and utilisation of Kangaroo Mother Care (KMC) in low- and middle-income countries (LMICs). Despite the empirical evidence of KMC optimising low-birth-weight infants’ (LBWIs’) survival, its advantages and the LMICs implementing the service, studies have shown that LBW infant deaths occurring in LMICs are largely contributing to global child mortality. The aim of this scoping review is to map out the literature on barriers, challenges and facilitators of KMC utilisation by parents with LBWIs.Methods and analysisThis scoping review will use Endnote X7 reference management software to manage articles. The review search strategy will use SCIELO and LILACS databases. Other databases will be used via EBSCOHost search engine and these are Academic search complete, CINAHL with full text, Education source, Health source: Nursing/Academic Edition, Medline with full text and Medline. We will also use Google Scholar, JSTOR, Open grey search engines and reference lists. A two-phase search mapping out process will be done. In phase 1, one reviewer will perform the title screening and removal of duplicates. Two reviewers will do a parallel abstract screening according to eligibility criteria. Phase 2 will involve the reading of full articles and exclusion of articles, in accordance with the eligibility criteria. Data extraction from the articles will be done by two reviewers independently and parallel to the data extraction form.The data quality assessment of the eligible studies will be done using the Mixed Method Appraisal Tool (MMAT). The extraction of the synthesised results and thematic content analysis of the studies will be done by NVIVO version 10.DiscussionWe expect to find studies on barriers, challenges and facilitating factors of KMC utilisation by parents with LBWIs in LMICs. The review outcomes will guide future research and practice and inform policy. The findings will be disseminated in print, electronic and conference presentations related to maternal child and neonatal health.

An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been recognized as a promising method for solving multi-objective optimization problems (MOPs), receiving a lot of attention from researchers in recent years. However, its performance in handling MOPs with complicated Pareto fronts (PFs) is still limited, especially for real-world applications whose PFs are often complex featuring, e.g., a long tail or a sharp peak. To deal with this problem, an improved MOEA/D (named iMOEA/D) that mainly focuses on bi-objective optimization problems (BOPs) is therefore proposed in this paper. To demonstrate the capabilities of iMOEA/D, it is applied to design optimization problems of truss structures. In iMOEA/D, the set of the weight vectors defined in MOEA/D is numbered and divided into two subsets: one set with odd-weight vectors and the other with even-weight vectors. Then, a two-phase search strategy based on the MOEA/D framework is proposed to optimize their corresponding populations. Furthermore, in order to enhance the total performance of iMOEA/D, some recent developments for MOEA/D, including an adaptive replacement strategy and a stopping criterion, are also incorporated. The reliability, efficiency and applicability of iMOEA/D are investigated through seven existing benchmark test functions with complex PFs and three optimal design problems of truss structures. The obtained results reveal that iMOEA/D generally outperforms MOEA/D and NSGA-II in both benchmark test functions and real-world applications.

Efficient Resource Constrained Scheduling Using Parallel Two-Phase Branch-and-Bound Heuristics

Branch-and-bound (B&B) approaches are widely investigated in resource constrained scheduling (RCS). However, due to the lack of approaches that can generate a tight schedule at the beginning of the search, B&B approaches usually start with a large initial search space, which makes the following search of an optimal schedule time-consuming. To address this problem, this paper proposes a parallel two-phase B&B approach that can drastically reduce the overall RCS time. This paper makes three major contributions: i) it proposes three partial-search heuristics that can quickly find a tight schedule to compact the initial search space; ii) it presents a two-phase search framework that supports the efficient parallel search of an optimal schedule; iii) it investigates various bound sharing and speculation techniques among collaborative tasks to further improve the parallel search performance at different search phases. The experimental results based on well-established benchmarks demonstrate the efficacy of our proposed approach.

Abutment Disconnection/Reconnection Affects Peri-implant Marginal Bone Levels: A Meta-Analysis.

Preclinical and clinical studies have shown that marginal bone loss can be secondary to repeated disconnection and reconnection of abutments that affect the peri-implant mucosal seal. The aim of this systematic review and meta-analysis was to evaluate the impact of abutment disconnections/reconnections on peri-implant marginal bone level changes. To address this question, two reviewers independently performed an electronic search of three major databases up to October 2015 complemented by manual searches. Eligible articles were selected on the basis of prespecified inclusion and exclusion criteria after a two-phase search strategy and assessed for risk of bias. A random-effects meta-analysis was performed for marginal bone loss. The authors initially identified 392 titles and abstracts. After evaluation, seven controlled clinical studies were included. Qualitative assessment of the articles revealed a trend toward protective marginal bone level preservation for implants with final abutment placement (FAP) at the time of implant placement compared with implants for which there were multiple abutment placements (MAP). The FAP group exhibited a marginal bone level change ranging from 0.08 to 0.34 mm, whereas the MAP group exhibited a marginal bone level change ranging from 0.09 to 0.55 mm. Meta-analysis of the seven studies reporting on 396 implants showed significantly greater bone loss in cases of multiple abutment disconnections/reconnections. The weighted mean difference in marginal bone loss was 0.19 mm (95% confidence interval, 0.06-0.32 mm), favoring bone preservation in the FAP group. Within the limitations of this meta-analysis, abutment disconnection and reconnection significantly affected peri-implant marginal bone levels. These findings pave the way for revisiting current restorative protocols at the restorative treatment planning stage to prevent incipient marginal bone loss.

Improved marching tetrahedra algorithm based on hierarchical signed distance field and multi-scale depth map fusion for 3D reconstruction

3D reconstruction systems are promoted by developments of both computer hardware and computing technologies. They still remain problems like high expense, low efficiency and inaccuracy. Especially for large-scale scenes, lack of full use of multi-scale depth information will cause blurring and irreal reconstruction results. To solve this problem, we construct the structure of hierarchical signed distance field (H-SDF) and design an improved marching tetrahedra algorithm for multi-scale depth map fusion. In addition, to improve efficiency, we also propose a two-phase search strategy in image feature matching: the bag-of-features model (BOF) is adopted in a coarse search to narrow search scope and then the SIFT descriptor is used in exact matching to pick reconstruction image points. Experiment results indicate that coarse search makes matching time shorter; using the H-SDF to fuse multi-scale depth maps, and isosurface extraction with improved marching tetrahedra algorithm can improve visual effect.