A Hybrid Algorithm for Pattern Matching: An Integration of Berry-Ravindran and Raita Algorithms

With the exponential growth of biological databases, it is a highly challenging and essential task to find the exact string from these databases. Various string pattern searching strategies played an essential role in solving string search problems. Mainly, the string pattern matching algorithm aims to reduce the running time which depends on attempts and character comparisons. Our paper proposed a new hybrid strategy, the Largest Shift Algorithm (LSA), to solve these string pattern matching problems more effectively. Our suggested new hybrid algorithm combines the most advantageous characteristics of Berry Ravindran, Zhu-Takaoka, and a customized Back & Forth Matching (BFM) algorithm. These three algorithms were chosen as they perform better in the tests for counting attempts and character comparisons. Three distinct types of algorithms were tested to analyze the performance of our proposed LSA algorithm, discussed in the literature which are BRR, maximum-shift, and quick-search algorithms. We have used English text, DNA, and protein sequences as data for their different nature. The proposed algorithm outperforms the previous algorithms in terms of performance such as runtime, total attempts, and comparisons of character.

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

The classical job-shop scheduling problem is one of the most difficult combinatorial optimization problems. Scheduling is defined as the art of assigning resources to tasks in order to insure the termination of these tasks in a reasonable amount of time. Job shop scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Mathematical and heuristic methods are the two major methods for resolving JSP. Job shop Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions. In this paper, a Hybrid algorithm combined artificial immune system and sheep flock heredity model algorithm is used for minimizing the total holding cost for different size benchmark problems. The results show that the proposed hybrid algorithm is an effective algorithm that gives better results than other hybrid algorithms compared in literature. The proposed hybrid algorithm is a good technique for scheduling problems.

This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information communication according to the human emotion-value relationship, and summarizes the characteristics of English emotion expression and information communication. Secondly, using artificial intelligence technology, it is proposed to construct an analysis model for English text emotion and information communication using the BiLSTM neural network. To deal with the characteristics of English text quickly and efficiently, it is necessary to encode the emotional information of English text, and based on encoding, the BiLSTM neural network is applied to extract the emotional features of English text and solve the problem of the loss of emotional features through the loss function. Then, the crawler tool is used to obtain the dataset from the Chinese English module under the MOOC of Chinese universities, and the evaluation indexes are set according to the model’s performance, followed by the experimental analysis of the English text emotion expression and information conveyance. The results show that compared with the original CNN, LSTM, and T-LSTM, the BiLSTM-based neural network performs better in the task of text emotion expression and information conveyance, with the accuracy rate staying above 0.925, and the effect on the English dataset is a bit better than that on the Chinese dataset. This study aims to enhance English teaching and communication between Chinese and foreign cultures.

Since Markowitz's seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.

Fast convergence optimization model for single and multi-purposes reservoirs using hybrid algorithm

String matching is considered as one of the center issues within the field of computer science, where there are numerous computer applications that supply the clients with string matching services. The increment within the number of databases which are created and protected in numerous computer gadgets had impacted researchers with the slant towards getting robust techniques in tending to this issue. In this study, the Maximum-Shift string matching algorithm is chosen to be executed with multi-core innovation through the utilization of OpenMP paradigm, in order to decrease the successive time, and increment the speedup and efficiency of the algorithm. The deoxyribonucleic acid (DNA), protein and the English text datasets are utilized to test the parallel execution that influences the Maximum-Shift algorithm execution when utilized with multi-core environment. The results demonstrated that the execution is affected by the performance between the parallel and consecutive execution of Maximum-Shift algorithm by data type. The English text appeared ideal comes about within the parallel execution time as compared to other datasets, whereas the DNA database set appeared the most elevated comes about when compared to other data types in terms of speedup and efficiency capabilities.

Vehicle navigation is one of the important applications of the single-source single-target shortest path algorithm. This application frequently involves large scale networks with limited computing power and memory space. In this study, several heuristic concepts, including hierarchical, bidirectional, and A*, are combined and used to develop hybrid algorithms that reduce searching space, improve searching speed, and provide the shortest path that closely resembles the behavior of most road users. The proposed algorithms are demonstrated on a real network consisting 374,520 nodes and 502,485 links. The network is preprocessed and separated into two connected subnetworks. The upper layer of network is constructed with high mobility links, while the lower layer comprises high accessibility links. The proposed hybrid algorithms are implemented on both PC and hand-held platforms. Experiments show a significant acceleration compared to the Dijkstra and A* algorithm. Memory consumption of the hybrid algorithm is also considerably less than traditional algorithms. Results of this study showed the hybrid algorithms have an advantage over the traditional algorithm for vehicle navigation systems.

Network representation is a convenient and intuitive abstraction for analyzing the massive interacting data. Some topological characteristics of the network have been found in the past decade, and community structure is the typical one of them. Community detection has become a hot topic in complex network analysis. In the paper, a hybrid algorithm is presented for solving such problem. At first, we take the max-degree node as the core node. Then, the diffusing operation is per-formed on it to produce two preliminary partitions. Subsequently, an EO-based adjustment step is used for generating the final partitioning with high quality. In addition, four real-world networks are used for validating the efficiency and effectiveness of our hybrid algorithm. The experimental results show that the proposed hybrid algorithm is a promising solution for solving the community detection problem both in precision and efficiency.

Optimal selection of retailers for a manufacturing vendor in a vendor managed inventory system

Since head-related transfer functions (HRTFs) represent the interactions between sounds and physiological structures of listeners, anthropometric parameters represent a straightforward way to customize (or predict) individualized HRTFs. This paper proposes a hybrid algorithm for predicting median-plane individualized HRTFs using anthropometric parameters. The proposed hybrid algorithm consists of three parts: decomposition of HRTFs; selection of key anthropometric parameters; and establishing a prediction formula. Firstly, an independent component analysis (ICA) is applied to median-plane HRTFs from multiple subjects to obtain independent components and subject-dependent weight coefficients. Then, a factor analysis is used to select key anthropometric parameters relevant to HRTFs. Finally, a regression formula that connects ICA weight coefficients to key anthropometric parameters is established by a multiple linear regression. Further, the effectiveness of the proposed hybrid algorithm is verified by an objective evaluation via spectral distortion and a subjective localization experiment. The results show that, when compared with generic Knowles Electronics Manikin for Acoustic Research (KEMAR) HRTFs, the spectral characteristics of the predicted HRTFs are closer to those of the individualized HRTFs. Moreover, the predicted HRTFs can alleviate front–back and up–down confusion and improve the accuracy of localization for most subjects.

A hybrid algorithm of particle swarm optimization (PSO) and local search (LS) is proposed to solve the redundancy allocation problem for series-parallel multi-state systems. The proposed hybrid algorithm is able to design the system structure with a minimum cost to provide a desired level of availability. Unlike most of the previous studies that only consider homogeneous redundancy, the proposed algorithm allows for the heterogeneous redundancy technique. The universal generating function method is applied to evaluate system availability. The standard PSO is modified and novel LS strategies are integrated to adapt to the redundancy allocation problem. Case studies that facilitate comparisons between the proposed hybrid algorithm and other non-hybrid heuristics as well as meta-heuristics reported in the literature (such as genetic algorithms, tabu search, and ant colony optimization) are provided. The results illustrate the advantages of the proposed hybrid algorithm in terms of the solution quality or algorithm efficiency.

High integration densities and design complexity of printed-circuit boards make board-level functional fault identification extremely difficult. Machine learning provides an opportunity to identify functional faults with high accuracy and thereby reduce repair cost. However, the large volume of manufacturing data comes in a streaming format and exhibits time-dependent concept drift in a production environment. These drawbacks limit the effectiveness of traditional machine-learning algorithms. We propose a diagnosis workflow that utilizes online learning to train classifiers incrementally with a small chunk of data at each step. These online-learning algorithms adapt to concept drift quickly with carefully designed update rules. A hybrid algorithm is also proposed to handle the scenario that data for varying numbers of boards are collected at different times. This hybrid algorithm concurrently implements two basic models. For each data chunk, this algorithm chooses the better model with high probability. The experimental results using two boards in high-volume production show that, with the help of online learning and the proposed hybrid algorithm, the F1-score for diagnosis based on binary classifiers can be improved from 57.3% to 81.0%. The top-3 accuracy for diagnosis based on multiclass classifiers can be improved from 78.3% to 91.4%.

Hybrid algorithm for short-term forecasting of PM2.5 in China

As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.