Parallel Artificial Bee Colony Algorithm Research Articles

Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm

AbstractInferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

An efficient network intrusion detection approach based on logistic regression model and parallel artificial bee colony algorithm

In recent years, the widespread use of the Internet has created many issues, especially in the area of cybersecurity. It is critical to detect intrusions in network traffic, and researchers have developed network intrusion and anomaly detection systems to cope with high numbers of attacks and attack variations. In particular, machine learning and meta-heuristic methods have been widely used for network intrusion detection systems (NIDS). However, existing studies on these systems usually suffer from low performance results such as accuracy, F1-measure, false positive rate, and false negative rate, and generally do not use automatic parameter tuning techniques. To address these challenges, this study proposes a novel approach based on a logistic regression model trained using a parallel artificial bee colony (LR-ABC) algorithm with a hyper-parameter optimization technique. The performance of the proposed model is evaluated against state-of-the-art machine learning and deep learning models on two publicly available NIDS datasets. Comparative performance evaluations show that the proposed method achieved satisfactory results with accuracy of 88.25% on the UNSW-NB15 dataset and 90.11% on the NSL-KDD dataset, and F1-measures of 88.26% and 90.15%, respectively. These findings demonstrate the efficacy of the proposed LR-ABC model in enhancing the accuracy and reliability, while providing a scalable solution to adapt to the dynamic and evolving landscape of cybersecurity threats.

A two-level parallel decomposition-based artificial bee colony method for dynamic multi-objective optimization problems

Many real-world multiple-objective optimization problems have objectives that change over time. These multiple-objective optimization problems are called dynamic multiple-objective optimization problems (DMOPs) and have received an increased attention. To track the changing Pareto front in DMOPs, the Pareto front at a certain moment needs to be obtained as efficiently as possible, which is challenging for most of existing methods. To this end, we propose a two-level parallel decomposition-based artificial bee colony method for solving DMOPs. To sufficiently accelerate the process of obtaining the Pareto front, a two-level parallel structure is designed in our method. In the first-level parallel structure, the dynamic multi-objective optimization problem at a certain moment is decomposed into a set of single-objective optimization problems that could be solved in parallel. In the second-level parallel structure, a parallel artificial bee colony algorithm is applied to solve each decomposed single-objective optimization problem. Specially, the parallel bee colony algorithm in our method is improved to support the exchange of information among neighbor problems, which is widely accepted to be effective in improving the efficiency of obtaining optimal solutions. To support the implementation of our improved parallel artificial bee colony algorithm, a two-level shared memory structure is designed. Our proposed method is compared with 4 widely used methods on CEC’ 2018 multi-objective optimization benchmarks and two constrained dynamic multi-objective optimization problems. The experimental results show that our method outperforms other compared methods in efficiency while maintaining good scalability and convergence.

Pipeline 3D Modeling Based on High-Definition Rendering Intelligent Calculation

In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyzes the mapping process of the standard mapping format, and then proposes a method of rendering panoramic video in the projection mapping format. By analyzing the parallel design schemes of swarm intelligence algorithms under different granularities, this paper proposes a parallel swarm intelligence optimization algorithm design method and then designs and implements a parallel artificial bee colony algorithm. With the help of the ArcGIS Engine development platform, this paper defines the interface for data exchange. With the support of Multipatch format data in ArcGIS software, through secondary development, the three-dimensional pipeline automatic modeling module is established, and the pipeline model is automatically generated. The digital construction and visualization of the company play a driving role. Based on the understanding of the characteristics of the pipeline image itself, combined with the analysis of the shortcomings of the existing methods, this paper proposes a new deep learning-based high-definition rendering solution for the pipeline image. In this paper, the pipeline image is preprocessed, and then the processed pipeline image is converted into a style pipeline image through the pipeline image style transfer technology, and the obtained style pipeline image is postprocessed to enhance the effect. The preprocessing of pipeline images mainly includes pipeline image enhancement and pipeline image filtering operations. Its purpose is to change the distribution of pipeline images to improve the quality of pipeline images and make them more suitable for subsequent style conversion. In the part of pipeline image style conversion, this paper proposes a new deep learning-based pipeline image high-definition rendering network, which consists of three subnetworks: pipeline image feature modeling module, feature model alignment module, and pipeline image re-rendering module. This article has conducted sufficient experiments to fully compare the processing results of the method proposed in this article and other existing methods and at the same time shows the high-quality high-definition rendering results. The experimental results verify the excellent performance of the method proposed in this paper.

A Secured and Intelligent Communication Scheme for IIoT-enabled Pervasive Edge Computing.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

An efficient three-Level parallel ABC algorithm for secondary structure prediction of complex RNA sequences

This paper introduces 3L-PABCfold, a parallel artificial bee colony algorithm with three-Level strategy for efficient secondary structure prediction of complex RNA sequences, by multi-processor parallel computing. Designed 3L-PABCfold is a combination of additionally proposed three sub-algorithms: three-Level parallel ABC algorithm (3L-PABC), set based ABC algorithm and discrete ABC (DABC) algorithm. Proposed 3L-PABCfold is an extension of author’s earlier developed TL-PSOfold Lalwani et al. (2016), a two-Level particle swarm optimization algorithm for RNA secondary structure prediction. 3L-PABCfold is furthermore enriched with complexity evaluation of highly obscure sequence families followed by makespan minimization and algorithm implementation in parallel computing environment.As the complex dataset, 1,00,000 RNA sequences are randomly generated with length range 139–6358 nucleotides. Level-I of the algorithm reduces the machines idle time by implementing an efficient schedule, whereas, Level-II maximizes the bonded pairs of RNA sequences and Level-III provides optimum secondary structure of RNA sequence. Moreover, three more parallel versions are developed i.e. parallel version of TL-PSOfold (TL-PPSOfold), three Level parallel version of TL-PSOfold (3L-PPSOfold) and two-Level parallel version of 3L-PABCfold (TL-PABCfold), so as to avail a fair comparison and to identify the competent strategy for the addressed problem. The performance evaluated at the criteria of sensitivity, specificity, F-measure along with the descriptive statistics and statistical significance, yields significantly better prediction accuracy for 3L-PABCfold. Further, the efficiency at the processing time criteria proves 3L-PABCfold as the most competent algorithm than TL-PPSOfold, TL-PABCfold and 3L-PPSOfold.

Parallel multi-objective artificial bee colony algorithm for software requirement optimization

In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development. Usually, due to lack of funds, lack of time and dependency between requirements, there is no possibility to develop all the required requirements. There are two conflicting objectives for choosing an optimal subset of the requirements: increasing customer satisfaction and reducing development costs. This problem is known as the next release problem (NRP) and is categorized as an NP-hard problem. Unlike the standard version of the NRP, we formulate this problem as a restricted multi-objective optimization problem. There exist metaheuristic algorithms for solving this problem performed as serials. In this paper, we introduce a parallel algorithm based on the master–slave model in order to improve the quality of the solutions. Based on the criteria of multi-objective problems, the quality of the obtained solution is compared with several metaheuristic algorithms. Two scenarios and two different datasets are used for experiments. Results indicate that the proposed method in the first scenario would highly improve the quality of solutions. Moreover, the method reduces execution time significantly through improvement in the quality of the solution in the second scenario.

Research and implementation of parallel artificial bee colony algorithm based on ternary optical computer

The artificial bee colony (ABC) algorithm is a widely used algorithm in the field of function optimization problems. The traditional ABC algorithm has long search time, slow convergence speed and easy to fall into local optimum at the end of the search. In this paper, the design scheme and method of implementing parallel ABC algorithm are studied, which makes use of the characteristics of many data bits and easy expansion of data bits of the ternary optical computer (TOC). First, by analysing the traditional ABC algorithm, we can find the parallel parts and parallel design. Then the detailed algorithm implementation flow is given and the clock cycle of the algorithm is analysed. Finally, the correctness of the parallel scheme is verified by experiments. Compared with the ABC algorithm and parallel ABC algorithms based on computer (PABC), the ABC algorithm based on TOC (TOC-PABC) effectively shortens the search time, improves the optimization performance of complex multimodal function optimization problems and obtains a higher speedup.

A new emigrant utilization strategy for parallel artificial bee colony algorithm

Artificial bee colony (ABC) algorithm is one of the most important swarm intelligence based metaheuristics that models the foraging behavior of real honey bees. Like other swarm intelligence based optimization algorithms, ABC algorithm is intrinsically suitable for parallelization by using extensive computational power of the distributed or shared memory based architectures. In the vast majority of the studies, the whole bee colony is divided into equally sized subcolonies and evaluated concurrently for the parallelization purposes. However, when an algorithm is parallelized, some mechanisms should be modified or new techniques should be introduced. In this paper, a new emigrant creation utilization strategy also called swap model is introduced. The main idea lying behind the swap model is based on directly using the information sent by the topological neighbor to change the best solution of the current subcolony. For investigating possible contributions of the swap model on the performance of the parallel ABC algorithm, a set of experimental studies with different benchmark problems, number of subcolonies and migration periods was carried out. The results obtained from the experiments compared with the serial ABC algorithm and its some variants in addition to the conventional parallel implementation of the same algorithm. From the comparisons, it is concluded that the parallelization of the ABC with the swap model significantly improves the convergence speed of the algorithm while protecting the qualities of the solutions, speedup and efficiency values.

Paralel Yapay Arı Koloni Algoritması için Rastgele Kaynaklar ile Yeni Bir Göçmen Üretme Yaklaşımı

Dividing the whole population into subpopulations or subcolonies then evaluating them simultaneously is one of the most commonly used parallelization approaches to utilize the computational power of the current systems. However, this type of parallelization strategy decreases the population diversity because of the division of the entire population and needs migrations between subpopulations to maintain the solution diversity until the end of the iterations. In this study, we proposed a new emigrant creation strategy in which the parameters of the best food source being migrated to the neighbor subpopulation is modified with the more appropriate parameters of the randomly determined solution or solutions and investigated its effect on the performance of the parallel Artificial Bee Colony (ABC) algorithm. Experimental studies showed that newly proposed emigrant creation strategy based on randomized solutions significantly improved the convergence performance and solution qualities of parallel ABC algorithm compared to the its standard serial and ring neighborhood topology based parallel implementation for which the best solutions are directly used as emigrants.

An improved multi-cores parallel artificial Bee colony optimization algorithm for parameters calibration of hydrological model

Parameter optimization and calibration play a crucial role in the overall performance of hydrological models and the quality of hydrologic forecast results. The hydrological model is characterized by high complexity, a large number of parameters, high dimensionality and a large amount of data processing. Therefore, there are many computationally intensive tasks in model parameter optimization that require a large CPU processing time. To improve the optimization precision and performance for parameters optimization of the Xinanjiang model, a parallel Multi-core Parallel Artificial Bee Colony algorithm (MPABC) was proposed based on the hybrid hierarchical model and Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee the population’s diversity, improve the global convergence ability and avoid falling into the local optimum. And also in order to divide the complex computing task into several independent parallel sub-tasks on different cores, so as to take all the performance advantages of multi-core CPU. The experiment is divided into two parts. In the first part, the performance of the original serial ABC algorithm and the MPABC algorithm is analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve a speedup of 3.795 and an efficiency of 94.87% in solving complex problems. The MPABC algorithm could greatly improve the optimization efficiency. The second part is to select the Nash–Sutcliffe coefficient as the objective function and apply the MPABC and PPSO and PgGA algorithms to optimize the Xinanjiang hydrological model in the Heihe River Basin. The results showed that the MPABC algorithm can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process. Thus, the MPABC algorithm is an effective and feasible method to solve the hydrological model parameters’ optimization problem, and can provide a reliable parameter decision support for practical applications of hydrological forecasting.

A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. Many heuristic algorithms such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, and ant colony optimization have been presented to solve it. Parallelization is one of the best approaches that can be used to enhance the performance of these heuristic algorithms. In this paper, we propose a parallel artificial bee colony algorithm to solve the job shop scheduling problem. In this method, artificial bee colony algorithm consists of several colonies that locate on different hosts of the network and algorithm is carried out in various colonies in parallel manner. The communication between colonies is carried out by exchanging migrants. A dynamic migration strategy is used to determine when a colony must communicate by its neighbors. Benchmark instances are used to investigate the performance of the proposed approach. The results show that the proposed parallel artificial bee colony algorithm improves the efficiency.

A Novel Parallel Artificial Bee Colony Algorithm and Its PAPR Reduction Performance Using SLM Scheme in OFDM and MIMO-OFDM Systems

In this letter, we propose a parallel artificial bee colony (P-ABC) algorithm based on a new search strategy. Performance of the P-ABC is tested for peak-to-average power ratio (PAPR) reduction problem using selected mapping (SLM) scheme in both orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO)-OFDM systems. Moreover, standard ABC and two different modified ABC algorithms—ABC/best/1 and modified ABC/best/1—are proposed for the SLM scheme. Simulation results show that the proposed P-ABC provides good PAPR reduction and bit-error-rate performances with low computational complexity.

Efficient Parallel Artificial Bee Colony Algorithm for Cooperative Spectrum Sensing Optimization

In this paper, the multiuser linear cooperative spectrum sensing optimization problem in cognitive radio system where the primary user (PU) and cognitive radio (CR) employ multiple antennas is investigated. By optimizing the different weights assigned on the statistic of each CR given a targeted probability of false alarm, the cooperative spectrum sensing optimization focuses on maximizing the probability of detection. Statistical characteristics of parameters in cooperative spectrum sensing for the PU with a single antenna and the CR with multiple antennas (SPMC) system, the PU with multiple antennas and the CR with a single antenna (MPSC) system, both the PU and the CR with multiple antennas (MPMC) system as well as the PU with Alamouti coding system have been investigated. Due to the non-convex characteristic of the cooperative spectrum sensing problem, an efficient parallel artificial bee colony (PABC) method motivated by the intelligent foraging behavior of a honeybee colony is introduced to address the problem without approximations and convexity constraints. Furthermore, classical benchmark functions are presented to validate the searching ability of the proposed algorithm. Simulation results showed that the PABC shows a superior ability when applied in both the typical test functions and the cooperative spectrum sensing scenario. Also, the reliability of spectrum sensing can be significantly promoted with the use of multiple antennas and Alamouti coding.

Parallel Artificial Bee Colony Algorithm for the Traveling Salesman Problem

Artificial Bee Colony Algorithm (ABCA) is a novel swarm intelligence algorithm which a colony of artificial bees cooperate in finding good solutions for numerical optimization problems and combinatorial optimization problems. Traveling Salesman Problem (TSP) is a famous combinatorial optimization problem which has been used in many fields such as network communication, transportation, manufacturing and logistics. However, it requires a considerably large amount of computational time and resources for solving TSP. To dealing with this problem, we present a Parallel Artificial Bee Colony Algorithm (PABCA) in several computers which operation system is Linux based on the Message Passing Interface (MPI). The entire artificial bee colony is divided into several subgroups by PABCA equally. Each subgroup performs an ABCA for TSP on each processor node, respectively. Each sub-colony on every processor node communicates the current best fitness function and parameters of current best fitness function according to ring topological structure during calculation process. Some well-known benchmark problems in TSP are used to evaluate the performance of ABCA and PABCA. Meanwhile, the performance of PABCA is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Experimental results show that the PABCA can obtain solutions with equal precision and reduce the time of computation obviously in comparison with serial ABCA. And PABCA have much better performance in contrast with GA and PSO.

Research of Parallel Artificial Bee Colony Algorithm Based on MPI

Artificial bee colony algorithm is a smart optimization algorithm based on the bees acquisition model. A long time for the search of the artificial bee colony algorithm, in this paper we propose a parallel algorithm of artificial bee colony algorithm (MPI-ABC), with an application of a parallel programming environment MPI, using the programming mode of message passing rewriting the serial algorithm in parallel. Finally, this paper compare both serial and parallel algorithm with testing on complex function optimization problems. The experimental results show that the algorithm is effective to improve the search performance, especially for high-dimensional complex optimization problem.

Profit based unit commitment: A parallel ABC approach using a workstation cluster

This paper proposes a parallel artificial bee colony (PABC) approach for committing generating units thereby maximizing the profit of generation companies. Profit based unit commitment (PBUC) must be obtained in a short time even though there is an increase in generating units. Nowadays, computing resources are available in plenty, and effective utilization of these resources will be advantageous for reducing the time complexity for a large scale power system. Here, the message passing interface based technique is used in the PABC algorithm in distributed and shared memory models. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. PABC for PBUC is tested for a power system ranging from 10 to 1000 generating units. Also the PABC is validated for economic dispatch and the unit commitment problem in a traditional power system on 40 and 10 unit systems, respectively.