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A triple-spark guiding strategy to enhance the loser-out tournament-based fireworks algorithm

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第4期20卷 7234-7252页

作者： Li, Sicheng Zhu, Junhao Han, Mingzhang Fan, Mingjie Zhao, Xinchao Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

Loser-out tournament-based fireworks algorithm (LoTFWA) is a new baseline of fireworks algorithm (FWA). However, its ability to search deeply in local areas and communicate among fireworks is not satisfying enough. Therefore, this paper proposes a new triple-spark guiding strategy for LoTFWA to deal with the mentioned problems. Among the three sparks generated for guiding, one is exactly the same as the original one in LoTFWA, the second one uses the centroid of good sparks to enhance the local exploitation, and the last one is based on Differential evolution (DE) mutation and used to enhance cooperation and exploration. Experimental results show that with low computational cost, the proposed guiding strategy attains significantly better results than LoTFWA and is competitive with state-of-the-art FWA variants. Furthermore, comprehensive experiments show that the proposed strategy has the potential to combine with other FWA variants to achieve better results.

关键词： single-object optimization fireworks algorithm loser-out tournament differential evolution guiding strategy

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Simplified hybrid fireworks algorithm

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KNOWLEDGE-BASED SYSTEMS 2019年 173卷 128-139页

作者： Chen, Yonggang Li, Lixiang Zhao, Xinchao Xiao, Jinghua Wu, Qingtao Tan, Ying Xuchang Univ Sch Informat Engn Xuchang 461000 Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Sci State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Beijing Peoples R China Henan Univ Sci & Technol Informat Engn Coll Luoyang 471023 Peoples R China

As a relatively new swarm intelligence algorithm, the fireworks algorithm (FWA) has been applied to solve lots of real-world optimization problem. However, there are still some shortcomings in the FWA algorithms. The search equation of FWA is relatively simple. Since the search mechanism of FWA mainly relies on the explosion sparks, the exploration and exploitation abilities of the algorithm are limited. In order to improve the performance of FWA, a simplified hybrid fireworks algorithm (SHFWA) is proposed in this paper. In SHFWA, to enhance the exploitation ability, a modified search formula is designed for core firework swarm. To enhance the exploration ability, for each firework swarm, another way of generating sparks-harmony spark is designed. In the conventional fireworks algorithm, the calculation of the number of sparks generated by each firework and the calculation of amplitude of explosion for each firework are very complex. In SHFWA, a simplified method is employed to compute these two variables. By introducing these methods, SHFWA is easy to implement and is good at exploration and exploitation. The proposed algorithm is tested on 40 benchmark functions. The experimental results demonstrate that SHFWA performs effectively and competitively when compared with several reported algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence fireworks algorithm Harmony mutation Global optimization

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fireworks algorithm for Dimensionality Resetting Based on Roulette 19th

Fireworks Algorithm for Dimensionality Resetting Based on Ro...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Yu, Senwu Li, Jun Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

ISBN: (纸本)9789819947546;9789819947553

In order to enhance the local search ability and global search ability of the fireworks algorithm (FWA), this paper proposes a fireworks algorithm for dimensionality resetting based on roulette (FWADRR). First, the algorithm improves the exponential decay explosion and mapping rules in EDFWA, avoid the interference in the process of the algorithm using regional information, and enhance the local search ability of the algorithm. Then, a new firework reset method is proposed, which uses roulette to reset a certain dimension of the optimal firework based on the change of the optimal firework position, and uses this position as the position for resetting the firework. Change the explosion range of the reset firework to the explosion range of the optimal firework to ensure that the reset firework and the optimal firework are in the same search stage. This method ensures that other fireworks can play a role until the algorithm converges, and improves the global search ability of the algorithm. Tested on the CEC2020 benchmark suite, the experimental results show that FWADRR significantly outperforms previous fireworks algorithms.

关键词： fireworks algorithm Roulette-Based Dimension Reset CEC2020

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Optimization of economic/environmental operation management for microgrids by using hybrid fireworks algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2017年第12期27卷 n/a-N.PAG页

作者： Wang, Zhenyu Zhu, Qibing Huang, Min Yang, Bao Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Oakland Univ Dept Elect & Comp Engn Rochester MI 48309 USA

The wide use of renewable energy sources stimulates the implementation of microgrid (MG) systems. MG is an independent power system that consists of various distributed generations (DGs), storage devices, and loads. The optimization problems of MG have gained wide spread interest considering the economic and environmental operation management of the system. To address the multi-variable non-linear programming problem with multiple hybrid constraints, this paper proposes a novel hybrid multi-objective fireworks algorithm with gravitational search operator (MFAGSO). In the algorithm, the operator, as a new search mechanism, directs the sparks in the clustered region to exchange positional information with Pareto-optimal solutions at each iteration process for obtaining accurate results. The operation management of typical grid-connected MG, which comprises various DGs and storage battery, was optimized within 24hours to evaluate the performance of the proposed algorithm. Considering operation cost and pollutant emission individually or simultaneously, 3 scenarios were involved in operation management for power market planning and practical considerations. The simulation results of the proposed algorithm were compared with those from other methods employed in previous literature and original fireworks algorithm. The results demonstrated the superior performance of the proposed method to solve operation management for optimizing problems of MG.

关键词： economic and environmental operation management fireworks algorithm gravitational search microgrid multi-objective optimization

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Accurate Compensation of Armored Thermocouple Based on fireworks algorithm

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IEEE SENSORS JOURNAL 2021年第8期21卷 9909-9918页

作者： Xu, Bo Han, Tailin Liu, Hong Wang, Xiao Ju, Mingchi Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Optoelect & Informat Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Sci & Technol Changchun 130022 Peoples R China

Measurement error occurs if the requirements of undistorted measurements cannot be satisfied by dynamic component characteristics of the sensor. Amplified time constant and longer thermal response time of armored thermocouple is caused by its additional protective cover structure and the inherent thermal inertia of the internal temperature measuring element. Therefore, there is hysteresis dynamic characteristic in actual measurement of sensor. This paper proposes an accurate compensation of armored thermocouple based on fireworks algorithm that balances the contradiction between the accuracy of the existing solution and the complexity of the solution, corrects the measured thermocouple temperature signal. Weight factor is innovatively introduced to design the fitness function of fireworks algorithm, and the iterative process is sectionally executed that achieves the improvement of time constant and thermal response time of armored thermocouple. Finally, results are given in the simulation section. Dynamic compensation time is reduced from 537ms to 130ms, which is 75.79 lower than before compensation result. Moreover, the thermal response time is reduced from 345ms to 110ms, which is 68.16 lower than the before compensation result. From the experimental results, we can observe that the proposed method in this paper can broaden the working bandwidth of the thermocouple sensor through establishing dynamic mathematical model of thermocouple. After compensation, the dynamic characteristics of armored thermocouple are close to bare bead thermocouple without protective sleeve. The proposed method can replace easily damaged bare bead thermocouple to realize accurate measurement in harsh temperature measurement environments without need frequent replacement, which also improves economic benefits.

关键词： Heuristic algorithms Sensors Temperature measurement Time factors Optimization Sensor systems Sensor phenomena and characterization Weight factor segmented processing fireworks algorithm time constant thermal response time

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A best firework updating information guided adaptive fireworks algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第1期31卷 79-99页

作者： Zhao, Haitong Zhang, Changsheng Ning, Jiaxu Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) has significant performance on solving single objective problems, and has been applied broadly on a number of fields. To further improve its performance, a best firework updating information guided adaptive fireworks algorithm (PgAFWA) is proposed, in which the evolving process is guided by the direction from previous best firework to the current best firework from two aspects: amplifying the explosion amplitude on the direction that the best firework is updated, and making more sparks which are generated by the best firework distributed on this direction to further enhance the exploring ability on it. Numerical experiment on CEC2015 test suite was implemented to verify performance of the proposed algorithm. The experiment results indicated that the PgAFWA outperformed the compared algorithms in terms of both convergence speed and solving quality.

关键词： fireworks algorithm Best firework Adaptive fireworks algorithm Updating direction Explosion range

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An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第2期25卷 983-998页

作者： Yadav, Ashish Mohan Tripathi, Kuldeep Narayan Sharma, S. C. Indian Inst Technol Roorkee Roorkee Uttarakhand India

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.

关键词： Fog computing Hybrid scheduling fireworks algorithm Opposition learning

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A Novel Tent-Levy fireworks algorithm for the UAV Task Allocation Problem Under Uncertain Environment

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IEEE ACCESS 2022年 10卷 102373-102385页

作者： Yu, Jiayang Guo, Jiansheng Zhang, Xiaofeng Zhou, Chuhan Xie, Tao Han, Xue Air Force Engn Univ Equipment Management & UAV Engn Coll Xian 710051 Peoples R China Air Force Engn Univ Air Control & Nav Coll Xian 710051 Peoples R China Air Force Engn Univ Fundamentals Dept Xian 710051 Peoples R China

Recently, unmanned aerial vehicle (UAV) task allocation is a hot topic both in the civilian and military, while the research of considering uncertainty and multi-objective is still in its infancy. Firstly, based on the uncertainty theory, a mathematical model of the uncertain multi-objective UAV task allocation problem with uncertain variables in both objective function and constraint conditions is established. The expected value criterion and opportunity constraint are introduced to transform the model into a deterministic optimization model. Furthermore, because traditional fireworks algorithm (FWA) has the shortcomings of low solution accuracy and slow convergence speed in solving the UAV task allocation problem, a novel Tent-Levy FWA (TLFWA) based on discrete update process is designed by introducing integer coding, Tent chaotic mapping and Levy variation. Experimental results show that the mean cost calculated by TLFWA is 8.17% and 13.73% lower than that of FWA and particle swarm optimization algorithm respectively, which proves the effectiveness of TLFWA. This study provides a new way to solve multi-objective and uncertain decision-making problems.

关键词： Task analysis Resource management Uncertainty Autonomous aerial vehicles Optimization fireworks algorithm Measurement uncertainty Chaotic communication Levy variation tent chaotic mapping TLFWA UAV task allocation uncertainty theory

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Enhanced long short-term memory with fireworks algorithm and mutation operator

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JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 12630-12646页

作者： Gong, Changqing Wang, Xinyao Gani, Abdullah Qi, Han Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Malaysia Sabah Fac Comp & Informat Labuan 87000 Malaysia

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.

关键词： fireworks algorithm Long short-term memory Mutation operator Traffic forecast

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A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

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JOURNAL OF SUPERCOMPUTING 2022年第3期78卷 4236-4260页

作者： Yadav, Ashish Mohan Tripathi, Kuldeep Narayan Sharma, S. C. Indian Inst Technol Roorkee Roorkee Uttarakhand India

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.

关键词： Fog computing Hybrid scheduling fireworks algorithm Edge devices

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