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Highly oscillatory Volterra integral equations are frequently encountered in engineering applications. The Nyström-type method is an important numerical approach for solving such problems. However, there remains scope to further optimize and accelerate the Nyström method. This paper presents a novel Nyström-type method to efficiently approximate solutions to second-kind Volterra integral equations with highly oscillatory kernels. First, the unknown function is interpolated at Chebyshev points. Then the integral equation is solved using the Nyström-type method, which leads to a problem of solving a system of linear equations. A key contribution is the technique to express the fundamental Lagrange polynomial in matrix form. The elements of the matrix, which involves highly oscillatory integrals, are calculated by using the classical Fejér quadrature formula with a dilation technique. The proposed method is more efficient than the one proposed in the recent literature. Numerical examples verify the efficiency and accuracy of the proposed method., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Wu, Sun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters.

The Hamiltonian p‐median problem consists of finding p(p$$ p $$ is given) non‐intersecting Hamiltonian cycles in a complete edge‐weighted graph such that each cycle visits at least three vertices and each vertex belongs to exactly one cycle, while minimizing the total cost of pcycles. In this work, we present an effective and scalable hybrid genetic algorithm to solve this computationally challenging problem. The algorithm combines an edge‐assembly crossover to generate promising offspring solutions from high‐quality parents, and a multiple neighborhood local search to improve each offspring solution. To promote population diversity, the algorithm applies a mutation operator to the offspring solutions and a quality‐and‐distance update strategy to manage the population. We compare the method to the best reference algorithms in the literature based on three sets of 145 popular benchmark instances (with up to 318 vertices), and report improved best upper bounds for eight instances. To evaluate the scalability of the method, we perform experiments on a new set of 70 large instances (with up to 1060 vertices). We examine the contributions of key components of the algorithm. [ABSTRACT FROM AUTHOR]

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. [ABSTRACT FROM AUTHOR]

A solar power plant is a large-scale photovoltaic (PV) system designed to supply usable solar power to the electricity grid. Building a solar power plant needs consideration of arrangements of several important components, such as PV arrays, solar inverters, combiner boxes, cables, and other electrical accessories. The design of solar power plants is very complex because of various optimization parameters and design regulations. In this study, we address the cable-routing problem arising in the planning of large-scale solar power plants, which aims to determine the partition of the PV arrays, the location of combiner boxes, and cable routing such that the installation cost of the cables connecting the components is minimized. We formulate the problem as a mathematical programming problem, which can be viewed as a generalized capacitated minimum spanning tree (CMST) problem, and then devise a branch-and-price-and-cut (BPC) algorithm to solve it. The BPC algorithm uses two important valid inequalities, namely the capacity inequalities and the subset-row inequalities, to tighten the lower bounds. We also adopt several acceleration strategies to speed up the algorithm. Using real-world data sets, we show by numerical experiments that our BPC algorithm is superior to the typical manual-based planning approach used by many electric power planning companies. In addition, when solving the CMST problem with unitary demands, our algorithm is highly competitive compared with the best exact algorithm in the literature. [ABSTRACT FROM AUTHOR]

We propose an effective multi-wave algorithm organized in multiple search phases for the max-mean dispersion problem, which offers enhancement of neighborhood search algorithms by incorporating the notion of persistent attractiveness in memory based strategies. In each wave, a vertical phase and a horizontal phase are first alternated to reach a boundary solution. Then a concluding horizontal phase is executed to search around this boundary solution for further solution refinement. Finally, an oscillation phase and a diversified initial solution generation phase focus on search diversification to build well-diversified initial solutions for subsequent waves and passes. Experimental results show that the proposed approach performs quite competitive with state-of-the-art algorithms in the literature. Additional analysis discloses the benefits of the key ingredients in the proposed algorithm. [ABSTRACT FROM AUTHOR]

Since data and resources have massive feature and feature of data are increasingly complex, traditional data structures are not suitable for current data anymore. Therefore, traditional single-label learning method cannot meet the requirements of technology development and the importance of multi-label leaning method becomes more and more highlighted. K-Nearest Neighbor (KNN) classification method is a lazy learning method in data classification methods. It does not need data training process and theoretical system is mature. In addition, principle and implementation is simple. This paper proposed improvements strategies only considers numerical feature of sample KNN when classifying, but not consider the disadvantage of sample structure feature. This paper introduced particle swarm optimization algorithm into KNN classification and make adjustments to Euclidean distance formula in traditional KNN classification algorithm and add weight value to each feature. Using adjusted distance formula to train training data through particle swarm optimization algorithm and optimized a set of weight value for all features and put these optimized weight values to adjusted distance formula and calculated the distance between each example in test data set and in training data set and predict the test data set. Experiment results show that weighted KNN classification algorithm based on particle swarm optimization algorithm can achieve better classification accuracy than traditional KNN classification algorithm. [ABSTRACT FROM AUTHOR]

Multi-label classification is to assign an instance to multiple classes. Naive Bayes (NB) is one of the most popular algorithms for pattern recognition and classification. It has a high performance in single label classification. It is naturally extended for multi-label classification under the assumption of label independence. As we know, NB is based on a simple but unrealistic assumption that attributes are conditionally independent given the class. Therefore, a double weighted NB (DWNB) is proposed to demonstrate the influences of predicting different labels based on different attributes. Our DWNB utilizes the niching cultural algorithm (NLA) to determine the weight configuration automatically. Our experimental results show that our proposed DWNB outperforms NB and its extensions significantly in multi-label classification. [ABSTRACT FROM AUTHOR]

As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm's shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance. [ABSTRACT FROM AUTHOR]

Composing medical crews with equity and efficiency is an important practical problem commonly arising from health care system management. This work presents the first hybrid memetic algorithm for this problem. The proposed approach integrates an original backbone-based crossover for generating promising offspring solutions and a tabu search based local optimization algorithm exploring both feasible and infeasible search regions. Computational experiments on two sets of benchmark instances in the literature are conducted to assess the proposed algorithm with reference to existing methods. This study advances the state-of-the-art of solving this relevant practical problem and is expected to inspire new solution methods to similar problems. • Backbone-based crossover combined with a dedicated tabu search. • Tabu search alternates between exploration in feasible and infeasible regions. • Crossover is based on preserving individuals shared by selected parents. • Competitive results compared to existing state-of-the-art heuristics. • The key ingredients of the proposed method are analyzed. [ABSTRACT FROM AUTHOR]

Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search problems, decreasing diversity and leading to the prematurity of the population as the number of evolutionary iterations increases. The pre-stack AVO elastic parameter inversion is nonlinear with slow convergence, while the pigeon-inspired optimisation (PIO) algorithm has the advantage of fast convergence and better optimisation characteristics. In this study, based on the characteristics of the pre-stack AVO elastic parameter inversion problem, an improved PIO algorithm (IPIO) is proposed by introducing the particle swarm optimisation (PSO) algorithm, an inverse factor, and a Gaussian factor into the PIO algorithm. The experimental comparisons indicate that the proposed IPIO algorithm can achieve better inversion results. [ABSTRACT FROM AUTHOR]

Abstract In this study, we extend the traveling repairman problem with profits (TRPP) by considering the use of multiple vehicles, which occurs commonly in practice. We call the resulting problem the multiple traveling repairman problem with profits (MTRPP). In MTRPP, a fleet of identical vehicles is dispatched to visit a subset of nodes in order to collect time-dependent profits. To solve MTRPP, we present a highly effective memetic algorithm, which combines (i) a randomized greedy construction method for initial solution generation, (ii) a variable neighborhood search for local refinement, and (iii) a dedicated route-based crossover operator for solution recombination. Computational experiments performed on a large set of instances reveal the efficiency of the algorithm. Additionally, we demonstrate the relevance of our algorithm for the classic TRPP. The reported computational results serve as a benchmark for future researchers who wish to investigate the problem. Highlights • The multiple traveling repairman problem with profits (MTRPP) is introduced. • A mathematical model of MTRPP is formulated. • A memetic algorithm (MA) is proposed for solving MTRPP. • Computational study shows that the proposed MA effectively solves MTRPP. [ABSTRACT FROM AUTHOR]

The generalized bike-sharing rebalancing problem (BRP) entails driving a fleet of capacitated vehicles to rebalance bicycles among bike-sharing system stations at a minimum cost. To solve this NP-hard problem, we present a highly effective memetic algorithm that combines (i) a randomized greedy construction method for initial solution generation, (ii) a route-copy-based crossover operator for solution recombination, and (iii) an effective evolutionary local search for solution improvement integrating an adaptive randomized mutation procedure. Computational experiments on real-world benchmark instances indicate a remarkable performance of the proposed approach with an improvement in the best-known results (new upper bounds) in more than 46% of the cases. In terms of the computational efficiency, the proposed algorithm shows to be nearly two to six times faster when compared to the existing state-of-the-art heuristics. In addition to the generalized BRP, the algorithm can be easily adapted to solve the one-commodity pickup-and-delivery vehicle routing problem with distance constraints, as well as the multi-commodity many-to-many vehicle routing problem with simultaneous pickup and delivery. • A highly effective memetic algorithm (MA) for the bike-sharing rebalancing problem. • MA combines evolutionary local search with a route-copy-based crossover. • Improved best upper bounds are reported for 42 medium and large instances. • Adaptations of MA are able to provide competitive results for the related 1-PDVRPD and M-M-VRPSPD problems. [ABSTRACT FROM AUTHOR]