Showing papers on "Sorting published in 2021"

MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting

Abstract: This paper proposes a multi-objective Slime Mould Algorithm (MOSMA), a multi-objective variant of the recently-developed Slime Mould Algorithm (SMA) for handling the multi-objective optimization problems in industries. Recently, for handling optimization problems, several meta-heuristic and evolutionary optimization techniques have been suggested for the optimization community. These methods tend to suffer from low-quality solutions when evaluating multi-objective optimization (MOO) problems than addressing the objective functions of identifying Pareto optimal solutions’ accurate estimation and increasing the distribution throughout all objectives. The SMA method follows the logic gained from the oscillation behaviors of slime mould in the laboratory experiments. The SMA algorithm shows a powerful performance compared to other well-established methods, and it is designed by incorporating the optimal food path using the positive-negative feedback system. The proposed MOSMA algorithm employs the same underlying SMA mechanisms for convergence combined with an elitist non-dominated sorting approach to estimate Pareto optimal solutions. As a posteriori method, the multi-objective formulation is maintained in the MOSMA, and a crowding distance operator is utilized to ensure increasing the coverage of optimal solutions across all objectives. To verify and validate the performance of MOSMA, 41 different case studies, including unconstrained, constrained, and real-world engineering design problems are considered. The performance of the MOSMA is compared with Multiobjective Symbiotic-Organism Search (MOSOS), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multiobjective Water-Cycle Algorithm (MOWCA) in terms of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), Spacing, and Run-time. The simulation results demonstrated the superiority of the proposed algorithm in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. The results indicate the effectiveness of the proposed algorithm in solving complicated multi-objective problems. This research will be backed up with extra online service and guidance for the paper’s source code at https://premkumarmanoharan.wixsite.com/mysite and https://aliasgharheidari.com/SMA.html . Also, the source code of SMA is shared with the public at https://aliasgharheidari.com/SMA.html .

On Multicriteria Decision-Making Method Based on a Fuzzy Rough Set Model With Fuzzy $\alpha$ -Neighborhoods

Abstract: In this article, a novel fuzzy $\alpha$ -neighborhood operator with reflexivity is constructed and a new fuzzy rough set model based on the fuzzy $\alpha$ -neighborhood operator is proposed. Aiming at decision-making in information systems with real-valued information systems (RVISs), we first utilize data normalization method to effectively transform RVISs into information systems with fuzzy-valued information systems (FVISs). Then, we use the fuzzy $\alpha$ -neighborhood-based fuzzy rough set model to convert FVISs into information systems with intuitionistic fuzzy-valued information systems (IFVISs). By adopting the idea of the PROMETHEE II method, we develop three different sorting decision-making schemes on IFVISs, which consist of the subtraction of intuitionistic fuzzy numbers, sorting functions, and intimacy coefficients. Finally, numerical experiments demonstrate the effectiveness of our method. Comparative studies and Spearman rank correlation analyses explain the superiority of our schemes. Experimental results verify the stability of the performance of our strategy.

Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms

Abstract: The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions.

Double Parameters Fractal Sorting Matrix and Its Application in Image Encryption

Abstract: In the field of frontier research, information security has received a lot of interest, but in the field of information security algorithm, the introduction of decimals makes it impossible to bypass the topic of calculation accuracy. This article creatively proposes the definition and related proofs of double parameters fractal sorting matrix (DPFSM). As a new matrix classification with fractal properties, DPFSM contains self-similar structures in the ordering of both elements and sub-blocks in the matrix. These two self-similar structures are determined by two different parameters. To verify the theory, this paper presents a type of 2×2 DPFSM iterative generation method, as well as the theory, steps, and examples of the iteration. DPFSM is a space position transformation matrix, which has a better periodic law than a single parameter fractal sorting matrix (FSM). The proposal of DPFSM expands the fractal theory and solves the limitation of calculation accuracy on information security. The image encryption algorithm based on DPFSM is proposed, and the security analysis demonstrates the security. DPFSM has good application value in the field of information security.

Multiple-criteria decision-making sorting methods: A survey

Abstract: Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods’ trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research.

Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy

Abstract: The recently finalized research project “ZRR for municipal waste” aimed at testing and evaluating the automation of municipal waste sorting plants by supplementing or replacing manual sorting, with sorting by a robot with artificial intelligence (ZRR). The objectives were to increase the current recycling rates and the purity of the recovered materials; to collect additional materials from the current rejected flows; and to improve the working conditions of the workers, who could then concentrate on, among other things, the maintenance of the robots. Based on the empirical results of the project, this paper presents the main results of the training and operation of the robotic sorting system based on artificial intelligence, which, to our knowledge, is the first attempt at an application for the separation of bulky municipal solid waste (MSW) and an installation in a full-scale waste treatment plant. The key questions for the research project included (a) the design of test protocols to assess the quality of the sorting process and (b) the evaluation of the performance quality in the first six months of the training of the underlying artificial intelligence and its database.

Directed search and competitive search equilibrium: A guided tour

Abstract: This essay surveys the literature on directed search and competitive search equilibrium, covering theory and a variety of applications. These models share features with traditional search theory, but also differ in important ways. They share features with general equilibrium theory, but with explicit frictions. Equilibria are often efficient, mainly because markets price goods plus the time required to get them. The approach is tractable and arguably realistic. Results are presented for finite and continuum economies. Private information and sorting with heterogeneity are analyzed. While emphasizing issues and applications, we also provide several hard-to-find technical results.

Dynamic closest color warping to sort and compare palettes

Abstract: A color palette is one of the simplest and most intuitive descriptors that can be extracted from images or videos. This paper proposes a method to assess the similarity between color palettes by sorting colors. While previous palette similarity measures compare only colors without considering the overall palette combination, we sort palettes to minimize the geometric distance between colors and align them to share a common color tendency. We propose dynamic closest color warping (DCCW) to calculate the minimum distance sum between colors and the graph connecting the colors in the other palette. We evaluate the proposed palette sorting and DCCW with several datasets and demonstrate that DCCW outperforms previous methods in terms of accuracy and computing time. We validate the effectiveness of the proposed sorting technique by conducting a perceptual study, which indicates a clear preference for the results of our approach. We also demonstrate useful applications enabled by DCCW, including palette interpolation, palette navigation, and image recoloring.

Multi-objective Feature Selection with Missing Data in Classification.

Abstract: Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a+ bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study.

The cooperative sorting strategy for connected and automated vehicle platoons

Abstract: This paper presents a "cooperative vehicle sorting" strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in discrete time-space domain Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain The problem is solved by the deterministic A* algorithm with a stepwise strategy, where only one vehicle can move within a movement step The resultant shortest path is further optimized with an integer linear programming algorithm to minimize the sorting time by allowing multiple movements within a step To improve the algorithm running time and address multiple shortest paths, a distributed stochastic A* algorithm (DSA*) is developed by introducing random disturbances to the edge costs to break uniform paths (with equal path cost) Numerical experiments are conducted to demonstrate the effectiveness of the proposed DSA* method The results report shorter sorting time and significantly improved algorithm running time due to the use of DSA* In addition, we find that the optimization performance can be further improved by increasing the number of processes in the distributed computing system

A Platoon Regulation Algorithm to Improve the Traffic Performance of Highway Work Zones

Abstract: This paper presents a cooperative traffic control strategy to increase the capacity of nonrecurrent bottlenecks such as work zones by making full use of the spatial resources upstream of w...

Handling Constrained Multiobjective Optimization Problems via Bidirectional Coevolution.

Abstract: Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective functions and various constraints. Due to the presence of constraints, CMOPs' Pareto-optimal solutions are very likely lying on constraint boundaries. The experience from the constrained single-objective optimization has shown that to quickly obtain such an optimal solution, the search should surround the boundary of the feasible region from both the feasible and infeasible sides. In this article, we extend this idea to cope with CMOPs and, accordingly, we propose a novel constrained multiobjective evolutionary algorithm with bidirectional coevolution, called BiCo. BiCo maintains two populations, that is: 1) the main population and 2) the archive population. To update the main population, the constraint-domination principle is equipped with an NSGA-II variant to move the population into the feasible region and then to guide the population toward the Pareto front (PF) from the feasible side of the search space. While for updating the archive population, a nondominated sorting procedure and an angle-based selection scheme are conducted in sequence to drive the population toward the PF within the infeasible region while maintaining good diversity. As a result, BiCo can get close to the PF from two complementary directions. In addition, to coordinate the interaction between the main and archive populations, in BiCo, a restricted mating selection mechanism is developed to choose appropriate mating parents. Comprehensive experiments have been conducted on three sets of CMOP benchmark functions and six real-world CMOPs. The experimental results suggest that BiCo can obtain quite competitive performance in comparison to eight state-of-the-art-constrained multiobjective evolutionary optimizers.

MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems

Abstract: To handle the multiobjective optimization problems of truss-bar design, this paper introduces a new metaheuristic multiobjective optimization algorithm The proposed algorithm is based on a recently reported single objective version of the gradient-based optimizer (GBO) inspired by the gradients of Newton’s equations The proposed algorithm is called as multiobjective gradient-based optimizer (MOGBO), in which two operators, such as local escaping operator and gradient search rule, and few vector sets are utilized in the search phase and the elitist non-dominated sorting mechanism is used for agent sorting to find Pareto optimal solutions The proposed MOGBO is a posteriori method, and the traditional crowding distance mechanism is employed to confirm the coverage of the best solutions for the objectives of the given problem The performance of the proposed MOGBO algorithm is verified and validated on different test cases, including 15 unconstraint benchmark test suites and eight constraint multiobjective truss bar design problems To prove the superiority of the MOGBO algorithm, the performance is compared with state-of-the-art algorithms, such as multiobjective ant lion optimization (MOALO), multiobjective water cycle algorithm (MOWCA), multiobjective colliding bodies optimization (MOCBO), and non-dominated sorting gray wolf optimizer (NSGWO) in terms of metrics, such as hyper-volume, coverage, inverted generational distance, pure diversity, Spacing, Spread, coverage Pareto front, diversity maintenance, generational distance, and runtime The solutions obtained by the proposed MOGBO algorithm is highly accurate and requires less runtime than the other selected algorithms The obtained results also show the efficiency of the MOGBO in solving most of all the complex multiobjective problems This research will be further backed up with external guidance for the future research at https://premkumarmanoharanwixsitecom/mysite

Deterministic Sorting of Submicrometer Particles and Extracellular Vesicles Using a Combined Electric and Acoustic Field.

Abstract: Sorting of extracellular vesicles has important applications in early stage diagnostics. Current exosome isolation techniques, however, suffer from being costly, having long processing times, and producing low purities. Recent work has shown that active sorting via acoustic and electric fields are useful techniques for microscale separation activities, where combining these has the potential to take advantage of multiple force mechanisms simultaneously. In this work, we demonstrate an approach using both electrical and acoustic forces to manipulate bioparticles and submicrometer particles for deterministic sorting, where we find that the concurrent application of dielectrophoretic (DEP) and acoustophoretic forces decreases the critical diameter at which particles can be separated. We subsequently utilize this approach to sort subpopulations of extracellular vesicles, specifically exosomes ( 300 nm). Using our combined acoustic/electric approach, we demonstrate exosome purification with more than 95% purity and 81% recovery, well above comparable approaches.

Power System Event Identification Based on Deep Neural Network With Information Loading

Abstract: Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions.

Abstract: Stable isotope labeling of microbial taxa of interest and their sorting provide an efficient and direct way to answer the question "who does what?" in complex microbial communities when coupled with fluorescence in situ hybridization or downstream 'omics' analyses. We have developed a platform for automated Raman-based sorting in which optical tweezers and microfluidics are used to sort individual cells of interest from microbial communities on the basis of their Raman spectra. This sorting of cells and their downstream DNA analysis, such as by mini-metagenomics or single-cell genomics, or cultivation permits a direct link to be made between the metabolic roles and the genomes of microbial cells within complex microbial communities, as well as targeted isolation of novel microbes with a specific physiology of interest. We describe a protocol from sample preparation through Raman-activated live cell sorting. Subsequent cultivation of sorted cells is described, whereas downstream DNA analysis involves well-established approaches with abundant methods available in the literature. Compared with manual sorting, this technique provides a substantially higher throughput (up to 500 cells per h). Furthermore, the platform has very high sorting accuracy (98.3 ± 1.7%) and is fully automated, thus avoiding user biases that might accompany manual sorting. We anticipate that this protocol will empower in particular environmental and host-associated microbiome research with a versatile tool to elucidate the metabolic contributions of microbial taxa within their complex communities. After a 1-d preparation of cells, sorting takes on the order of 4 h, depending on the number of cells required.

Rapid Sorting and Regrouping of Retired Lithium-Ion Battery Modules for Echelon Utilization Based on Partial Charging Curves

Abstract: The lithium-ion battery of an electric vehicle continues to have available capacity even after it is retired, thus representing good echelon utilization value The ideal regrouping form for echelon utilization is conducted at the module level However, existing sorting methods are generally only suitable at the cell level To address this issue, a fast sorting and regrouping method is proposed at the module level based on a machine learning algorithm First, the correlation between the charging curve and the remaining useful capacity of the battery is investigated The charging curves of cells in a module are translated and supplemented to extract the capacity characteristics without disassembling the modules Next, a rapid sorting model based on the support vector machine is proposed to estimate the capacity Then, a regrouping method based on an improved K-means algorithm that considers different echelon utilization scenarios at the module level is proposed Finally, simulations and experiments are conducted to verify the effectiveness of the proposed method The results show that the capacity prediction accuracy is within 3%, and the consistency of the echelon utilization battery system obtained by the proposed regrouping method is higher than that obtained by the conventional method

A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center

Abstract: To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data center. The inefficient VM placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased intercommunication cost, and security breaches. To address the aforementioned challenges, a novel secure and multiobjective VMP (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs, which emphasizes secure and timely execution of user application by reducing intercommunication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state of the arts observed a notable reduction in shared servers, intercommunication cost, power consumption, and execution time up to 28.81%, 25.7%, 35.9%, and 82.21%, respectively with increased resource utilization up to 30.21%.

Development of image recognition software based on artificial intelligence algorithm for the efficient sorting of apple fruit

Abstract: Manual sorting of fruits was considered as a significant challenging for agricultural sector as it is a laborious task and may also lead to inconsistency in the classification. In order to improve the apple sorting efficiency and realize the non-destructive testing of apple, the machine vision technology integrated with artificial intelligence was introduced in this article for the design of apple sorting system. This article provides a low budget alternative solution for intelligent grading and sorting of apple fruit employing the deep learning-based approach. The automatic grading of apple was realized according to the determined apple grading standard by applying various stages of artificial intelligence platform like grayscale processing, binarization, enhancement processing, feature extraction and so on. The proposed end-to-end low-cost machine vision system provides an automated sorting of apple and significantly reduces the labor cost and provides a time-effective solution for medium and large-scale enterprises. In order to verify the feasibility of the scheme, the image recognition system of apple sorting machine is tested and the average accuracy of 99.70% is achieved while observing the recognition accuracy 99.38% for the CNN based apple sorting system. The results show that the sorting image recognition system can successfully sort apples according to the perimeter characteristics. It realizes the non-destructive testing and grade classification of apple and provides an important reference value for the research and development of fruit automatic sorting system.