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Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A mathematical model of heterogeneous task assignment problem based on mobile crowdsensing is established. This model considers the psychological and behavioral processes of participants, introduces environmental information, participants􀆳 health status, credibility, measurement time and other factors, and minimizes the total cost of task completion by finding the optimal task allocation scheme. The total task cost includes compensation cost, data loss cost and journey cost. To solve the model, a discrete fireworks algorithm with prediction information is proposed. This algorithm uses the integer coding, and the heuristic information of distance and matched-degree in the model are adopted to design the firework explosion operator. A grouping linear prediction strategy of explosion amplitude and an adaptive competition mechanism of mutation operator are proposed. Experimental results show that, compared with the existing algorithms, the proposed algorithm can find a better assignment scheme for the heterogeneous task assignment problem with mobile crowdsensing. [ABSTRACT FROM AUTHOR]

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm’s explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis. [ABSTRACT FROM AUTHOR]

A constrained multi-objective optimization model for the low-carbon-cold chain distribution of vaccines is established to minimize the corporate transportation costs including the cost of carbon emissions and customer dissatisfaction, satisfying the constraints of the number of available vehicles, vehicle capacity and time window. A discrete two-archive-based multi-objective fireworks algorithm is proposed. The decoding method that can meet the constraints of the number of available vehicles and vehicle capacity is adopted. The partial mapping explosion operator is designed. Feasible solution archive and infeasible solution archive are set for coevolution. Feasibility search is performed on the infeasible solution archive. Experimental results show that, compared with the existing algorithms, the proposed algorithm can effectively obtain a group of Pareto non-dominated solutions with better convergence and distribution. [ABSTRACT FROM AUTHOR]

Fireworks algorithm is a novel swarm intelligence optimization framework which focuses on the potential of collaboration among multiple subpopulations with independent search ability. Although it has been proved to perform excellently in many tasks, the collaborative mechanism of fireworks is still quite undeveloped. In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed and implemented. The local search of each firework is replaced by covariance matrix adaptation evolution strategy for efficient exploitation. A coordination strategy inspired from Voronoi diagram is proposed to approximate the theoretical model for stable global exploration. Experimental results show that the proposed algorithm not only outperforms previous variants of fireworks algorithm significantly, but also achieves competitive results compared with state‐of‐the‐art evolutionary algorithms, which are intensively fine‐tuned on the objective functions. [ABSTRACT FROM AUTHOR]

This study introduces a novel Parallel Hybrid Island architecture which shows a parallel way to combine different meta-heuristic algorithms by using the island model as the base. The corresponding hybrid algorithm is called Parallel Hybrid Island Metaheuristic Algorithms (PHIMA). The hybrid parallel structure exploits the characteristics of the individual metaheuristic algorithms to boost robustness and diversity. Island Genetic Algorithm has been combined with Particle Swarm Optimization and Fireworks Algorithm to build three different PHIMA algorithms: PSO-GA (PHIMA-PGA), FWA-GA (PHIMA-FGA) and FWA-PSO-GA (PHIMA-FPGA). Further, another implementational variation known as “co-evolution” is applied to the sub-GA islands of PHIMA-FPGA to improve the performance on multi-modal high-dimensional problems. This variation is referred to as PHIMA-FPGA-Co. Each PHIMA Algorithm exhibits different advantages and characteristics, and the parallel hybridization using the island model is found to improve robustness and population diversity. The performances of the four new algorithms are compared with each other and that of the traditional Island GAs and all four proposed PHIMA algorithms show better result quality.

The artificial fish swarm algorithm can achieve good convergence effects in the early stage, but in the late stage, the algorithm has the problems of slow convergence speed and low optimization accuracy, and it is easy to fall into local extremes, making the algorithm's convergence effect poor. Therefore, the characteristics of fireworks algorithm are used to improve the deficiency of fish swarm algorithm that is easy to fall into local extreme value in the late stage, and FWA-artificial fish swarm algorithm is put forward. When the effect of artificial fish swarm algorithm is poor, the explosion, mutation, mapping, and selection operations of fireworks algorithm are introduced to increase the variability of artificial fish, so as to enhance the optimization speed and ability of the algorithm. Finally, the improved algorithm is tested by four typical complex functions which are difficult to find the optimal solution by traditional method. Simulation results prove that the algorithm has the advantages of faster optimization speed, higher precision, and stronger stability. [ABSTRACT FROM AUTHOR]

To solve the problems of low identification accuracy and complex identification methods in the Bouc-Wen model of the magnetorheological (MR) damper, a new parameter identification method using the fireworks algorithm (FWA) is proposed. According to the experimental results of the dynamic characteristics of the MR damper and the simulation data of the Bouc-Wen model, the FWA is used to identify the seven parameters of the Bouc-Wen model. On the basis of the relationship between the identification results and the command current, the current-controlled Bouc-Wen model (I-Bouc-Wen model) is constructed and compared with the experimental results under different sinusoidal excitation frequencies. Compared with the genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO) algorithm, the FWA has the advantage of faster convergence, shorter calculation time, and higher stability in solving the parameter identification problem of the highly nonlinear hysteretic model. Under three harmonic excitations, the average calculation accuracies of the I-Bouc-Wen model reache 88.64 %, 90.45 %, and 81.28 %, respectively, and the dynamic characteristic curve of the model is in basic agreement with the experimental results. It can be used for the subsequent controller design and simulation research and lay a foundation for applying the parameterized model of the MR damper in vibration reduction control. [ABSTRACT FROM AUTHOR]

With the rapid advancements in processing and storage technology along with the popularity of the internet, computing capabilities have become more affordable, efficient, and widely accessible than ever before. This advancement has resulted in the emergence of a modern computing environment known as fog computing. Due to the latency-sensitiveness feature, computation of these services in fog computing is advantageous than cloud. Task scheduling is a significant issue in fog systems and substantially impacts resource utilization, task computation, and latency time. Many heuristic and meta-heuristic techniques have been applied to solve the scheduling issue. For the success of any meta-heuristic algorithm, an appropriate composition of exploration and exploitation of solution space is required to improve convergence and avoid local optima. To meet these requirements, a modified fireworks algorithm with the combination of opposition-based learning and differential evolution techniques is presented. Differential evolution operator has been utilized to avoid local optima and opposition-based learning technique has been applied for creating a diversified solution set of population. The proposed method works on the minimization of makespan and cost and improves resource utilization. The experiments have been carried out on a variety of workloads, and the findings have been compared with some recent popular metaheuristic techniques. The comparison has verified the importance of the proposed approach. [ABSTRACT FROM AUTHOR]

Simple firework algorithms get local optimal solutions easily and converge slowly, this paper adjusts the algorithm accordingly. Firstly, redefine the explosion operator by the aid of μ-law where the fitness value is converted to the degree of fitness, and the aberrance scope can be adjusted by the value of μ and the scaling factor. At the same time, the mapping strategy based on information exchange is used to enhance the search efficiency. In this paper, 12 general benchmark functions are used to compare the μFWA algorithm with other four algorithms. The experimental results show that the μFWA algorithm can obtain ideal results in the optimization of unimodal function and multimodal function which means that the algorithm can balance global search and local search well, and the convergence speed of the algorithm is improved obviously. [ABSTRACT FROM AUTHOR]

The fireworks algorithm (FWA) has achieved significant global optimization ability by organizing multiple simultaneous local searches. By dynamically decomposing the target problem and handling each one with a sub-population, it has presented distinct property and applicability compared with traditional evolutionary algorithms. In this paper, we extend the theoretical model of fireworks algorithm based on search space partition to obtain a hierarchical collaboration model. It maintains both multiple local fireworks for local exploitation and one global firework for overall population distribution control. The implemented hierarchical collaborated fireworks algorithm is able to combine the advantages of both classic evolutionary algorithms and fireworks algorithms. Several experiments are provided for in-depth analysis and discussion on the proposed algorithm. The effectiveness of proposed strategy is demonstrated on the benchmark test suite from CEC 2020. Experimental results validate that the hierarchical collaborated fireworks algorithm outperforms former fireworks algorithms significantly and achieves similar results compared with state-of-the-art evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal over-frequency generator tripping strategy aiming at implementing the least amount of generator tripping for the regional power grid with high penetration level of wind/photovoltaic (PV), to handle the over-frequency problem in the sending-end power grid under large disturbances. A steady-state frequency abnormal index is defined to measure the degrees of generator over-tripping and under-tripping, and a transient frequency abnormal index is presented to assess the system abnormal frequency effect during the transient process, which reflects the frequency security margin during the generator tripping process. The scenario-based analysis method combined with the non-parametric kernel density estimation method is applied to model the uncertainty of the outgoing power caused by the stochastic fluctuations of wind/PV power and loads. Furthermore, an improved fireworks algorithm is utilized for the solution of the proposed optimization model. Finally, the simulations are performed on a real-sized regional power grid in Southern China to verify the effectiveness and adaptability of the proposed model and method.

In order to solve the problems of serious unbalanced loading and large errors during the loading of bulk materials in coal mines, an intelligent loading system for bulk materials based on the fireworks algorithm (FWA) optimized recursive fuzzy neural network (RFNN) is proposed. By comparing the measured value and the set value of train carriage speed, the deviation is obtained as the input of RFNN controller. The deviation is processed by RFNN controller for fuzzification, dynamic memory adjustment and defuzzification. FWA is used to optimize RFNN weight so that RFNN controller can self-adaptively output the corrected control parameters. The traction motor frequency is obtained by the bulk material loading metering model, the material quality, material height and distance traveled during carriages loading collected by each sensor and the control parameters output from RFNN controller. Furthermore, the traction motor speed is changed so as to adjust the train carriage speed and realize the unbalanced loading of bulk materials. It has been proved that RFNN controller optimized by FWA can quickly adjust the carriages speed and keep the speed stable so as to meet the requirements of distributed and balanced loading of multiple carriages and improve the loading accuracy at the same time.

With the rapid increase in the functionality of IoT applications, the services provided by edge/IoT devices have surged in the recent past. Fog computing is gaining momentum as a contemporary computational framework for IoT-enabled smart applications that offers a latency sensitivity advantage over cloud computing. Effective task scheduling reduces application computation and latency durations while improves QoS. Many researchers have proposed a variety of heuristic and metaheuristic approaches for effective scheduling; however, there is still scope for improvement. The present work proposes a hybrid of heuristic and metaheuristic techniques for the scheduling of tasks. fireworks algorithm (FWA) is a metaheuristic algorithm, and Heterogeneous Earliest Finish Time (HEFT) is a heuristic algorithm. The bi-objective optimization approach is presented to minimize the makespan and cost factors. To compare the performance of the algorithm, the experiments have been conducted on distinct and independent scientific workflows. The results of exhaustive simulations have established the significance of the presented BH-FWA algorithm over other comparative approaches. To validate the relevance of the technique for fog computing networks, the metrics utilized for comparisons are makespan, cost, and throughput. [ABSTRACT FROM AUTHOR]

Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 presents new challenges for emergency management of medical supplies. Epidemic investigation and patient treatment are severely affected by problems such as difficult transportation, slow dispatch, and low transfer efficiency. In order to solve the problem of emergency dispatch of medical supplies under public health emergencies, a dual-objective emergency dispatch model of medical supplies is established. It takes the maximization of demand point satisfaction as the main goal, and the minimization of vehicle travel time as the secondary goal. The SEIR model is used to predict the amount of medical supplies required at the demand point to ensure the accuracy and simplicity of the model. The model uses the ideal point method to transform the dual-objective problem into a single-objective problem. Aiming at the characteristics of the model, an optimized firework algorithm is proposed to solve the model. It increases the ability of local optimization by changing the mutation strategy. In addition, it introduces the concept of tabu table to avoid the algorithm from falling into the local optimum. Finally, the optimized firework algorithm is proved to have more efficient performance. It can better highlight the fairness and rationality of the model, so as to quickly and reasonably complete the distribution of medical supplies, and to maximize the safety of patients' life. [ABSTRACT FROM AUTHOR]

Abstract In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

Wireless Sensor Network (WSN) as one of the representatives of the Internet of Things technology has also received much attention. To accurately diagnose fault sensor nodes, a fault diagnosis method based on fireworks algorithm optimization convolutional neural network algorithm is proposed. The weights and biases of the convolutional neural networks are optimized by using the self-regulating mechanism of global and local searching ability of fireworks algorithm. So the problem of convolution neural network in extreme judgment and limited convergence speed is solved, to effectively realize the fault diagnosis of the WSN. Simulation experiments show that this algorithm has higher fault diagnosis accuracy than other classic WSN fault diagnosis algorithms.

Aiming at the typical problems of deploying sonar buoys in appointed sea area, this paper summarizes two problems existing in previous studies and puts forward a quick deployment method for sonar buoys detection under the overview situation of underwater cluster targets. Firstly, considering the influence of an underwater target course on target strength, the overlapping coefficient ”buoy group” mode is introduced to deploy the array. And combining with the random distribution law of underwater targets in the exploration area, the mathematical optimization model for sonar buoys detection under the overview situation of underwater cluster targets is established. Then, the fitness function corresponding to the buoy deployment optimization model is defined, and the adaptive fireworks algorithm is used to solve the optimization problem for obtaining the sonar buoys deployment scheme. Finally, through the comparison and analysis of results for the seven group simulation experiments, the conclusions that are beneficial to improve the detection efficiency of the sonar buoy deployment are obtained. The proposed method can provide useful support for underwater cluster multi-target detection and the problem of counterattack underwater cluster multi-platform.

Prediction models are used to prevent and prepare for corresponding events according to various types of data generated in production. Aiming at the problems of lower predictive accuracy and slower convergent speed of the existing prediction models, a prediction model based on fireworks algorithm (FWA) and long short-term memory (LSTM) is proposed to predict time-related data. Firstly, we establish the interconnection structure model of the hidden layer nodes in the LSTM. Then, considering the diversity and concurrency of the group, we optimize the hyperparameters combination of LSTM. Finally, we improve FWA by adding three mutation operators (Gaussian mutation operator, Cauchy mutation operator, and discrete mutation operator). Based on the enhanced FWA, we achieve a better optimization effect by increasing the diversity of hyperparameters combination. The experimental results show that the performance of the proposed LSTM-enhanced FWA model has been significantly improved by comparing to the existing LSTM and LSTM-GS models. The mean absolute error (MAE) is reduced by 38.49% and 17.79%, respectively; the root mean squared error (RMSE) is reduced by 29.47% and 19.28%, respectively; and the mean absolute percentage error (MAPE) is reduced by 36.67% and 26.92%, respectively. MAE and MAPE represent the degree of deviation between the predicted value and actual value, and RMSE well reflects the precision. This means that the proposed LSTM-enhanced FWA model is better than existing LSTM and LSTM-GS models because all three types of error are reduced, meanwhile three kinds of error better reflect accuracy and precision of error. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network's lifetime is an area of constant study and innovation. The battery units of the sensor nodes cannot be recharged or replaced. Therefore, the need for energy efficiency in WSNs is ever-present. This paper proposes a Firework inspired Clustering Algorithm (FCA) to generate well defined and load-balanced clusters with the sensor nodes and gateways. The gateway works as cluster head (CH) for each cluster. The algorithm considers each cluster as a firework where the CH is the center of the firework and each sensor node is a 'spark' emitted by the firework. The goal of the FCA is to maximize the lifetime of the sparks which in turn will maximize the lifetime of the network. Simulations of the proposed algorithm are performed and compared with a few existing algorithms. The results show that the proposed algorithm outperforms under different evaluation metrics such as average energy consumed by sensor nodes vs number of rounds, number of active sensors vs number of rounds, first gateway die and half of the gateways die. [ABSTRACT FROM AUTHOR]