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International Conference on Computational Intelligence and Communication Technology (CICT)

作者： Li Jingmei Li Lanting Wang Jiaxiang Li He Harbin Engn Univ Coll Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538608869

To fully exploit the parallelism and efficiency of CMP architecture, improve the efficiency of thread scheduling under CMP architecture and avoid the drop of multi-core processor throughput due to resource contention, a thread scheduling strategy - IFWAC based on improved fireworks algorithm is proposed. IFWAC excludes the fireworks with the optimal value of each generation to ensure the effectiveness of the sparks and avoid the waste of computing resources;The nonlinear inertia weighting factor is applied to the dimension of the rejection operation. So that the algorithm in the early iteration has a stronger global exploration capabilities. In the latter part of the iteration can be meticulous optimization. The IFWAC, the fireworks algorithm and the genetic algorithm are used to perform the scheduling experiment on the Matlab platform. The experimental results show that the IFWAC has obvious advantages in the convergence speed and the accuracy of the solution. The IFWAC improve the throughput of multi-core processors and has a wide range of research value.

关键词： thread scheduling firework algorithm exclusion operation genetic algorithm

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Dynamic of firework algorithm Analyzed with Complex Network

Dynamic of Firework Algorithm Analyzed with Complex Network

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作者： Kadavy, Tomas Brno University of Technology

In this paper, a visualization of firework algorithm (FWA) inner dynamics as an evolving complex network is presented. Recent research in unconventional controlling and simulation of metaheuristic dynamics shows that this kind of visualization technique has been utilized only for algorithms with some social communication or behavior leading to sharing information across the population. However, provided analysis suggests that the network can identify some types of surface of tested functions.

关键词： firework algorithm FWA Complex Network Surface Analysis

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Improvement of the firework algorithm for Classification Problems

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Journal of Cyber Security 2020年第4期2卷 191-196页

作者： Yu Xue Sow Alpha Amadou Yan Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Attracted numerous analysts’consideration,classification is one of the primary issues in Machine *** evolutionary algorithms(EAs)were utilized to improve their global search *** the previous years,many scientists have attempted to tackle this issue,yet regardless of the endeavors,there are still a few *** on solving the classification problem,this paper introduces a new optimization classification model,which can be applied to the majority of evolutionary computing(EC)*** algorithm(FWA)is one of the EC methods,Although the firework algorithm(FWA)is a proficient algorithm for solving complex optimization *** proficient of the FWA isn't fulfilled when being utilized for solving the classification *** this paper we previously proposed optimization classification model according to the classification *** that point we legitimately utilize the model with FWA to solve the classification ***,to investigate the performance of our model,we select 4 datasets in the experiments,and the results indicate that an improved FWA can upgrade the classification accuracy by using this model.

关键词： Swarm intelligence algorithm firework algorithm evolutionary algorithm classification

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firework-based software project scheduling method considering the learning and forgetting effect

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SOFT COMPUTING 2019年第13期23卷 5019-5034页

作者： Guo, Yinan Ji, Jianjiao Ji, Junhua Gong, Dunwei Cheng, Jian Shen, Xiaoning China Univ Min & Technol Xuzhou Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Nanjing Jiangsu Peoples R China

The learning and forgetting ability, as the inherent characteristics of the employees, have a great impact on the software development process. By using the idea of WLC learning and VRVF forgetting models, an novel learning and forgetting model is constructed for software project scheduling problem to measure the dynamic skill levels. Taking the cost and duration as the optimization objectives, corresponding software project scheduling model considering the learning and forgetting effect is formed. The improved multi-objective firework algorithm with a novel encoding scheme is present. The crossover-explosion operator is introduced to enhance the information exchange between the better sparks or fireworks. A novel reservation strategy with two archives is proposed to avoid the ineffective search around the local optimum. Experimental results indicate that the learning ability plays a positive role on the optimal scheduling schemes and the forgetting effect is the opposite. The duration and cost of the project are inversely proportional to the learning coefficient and directly proportional to forgetting coefficient. By comparing the scheduling schemes from NSGA-II and original FA, the proposed method shows the better scheduling performances.

关键词： firework algorithm Learning effect Forgetting effect Software project scheduling

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A master-apprentice evolutionary algorithm for maximum weighted set K-covering problem

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APPLIED INTELLIGENCE 2023年第2期53卷 1912-1944页

作者： Zhou, Yupeng Fan, Mingjie Liu, Xiaofan Xu, Xin Wang, Yiyuan Yin, Minghao Northeast Normal Univ Coll Informat Sci & Technol Changchun Peoples R China Northeast Normal Univ Key Lab Appl Stat MOE Changchun Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China

The maximum weighted set k-covering problem (MWKCP) is a fundamental optimization problem, which depicts the application scenario of resource-constrained environment and user preference selection. In this paper, the mathematical formulation of MWKCP is given for the first time. Then, a novel master-apprentice evolutionary algorithm (MAE) is proposed for solving this NP-hard optimization problem. In order to make MAE applicable to MWKCP, a path re-linking operator is designed as the mutual learning process of two individuals, and a bare bones fireworks algorithm with explosion amplitude adaptation is adopted as the self-learning stage. Experimental results on 150 classical instances show that the proposed algorithm performs best among all competitors including an exact solver and three heuristic algorithms.

关键词： Maximum weighted set k-covering problem Master-apprentice evolutionary algorithm firework algorithm Path re-linking Local search

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Disassembly sequence planning using a fast and effective precedence-based disassembly subset-generation method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2020年第3期234卷 513-526页

作者： Zhang, Nan Liu, Zhenyu Qiu, Chan Cheng, Jin Tan, Jianrong Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Zhejiang Peoples R China

Disassembly sequence planning plays a crucial role in the reuse and remanufacturing of end-of-life products. However, it is challenging to obtain optimal disassembly sequences as it is a difficult non-deterministic polynomial-time-hard combinatorial optimisation problem. This study proposes a precedence-based disassembly subset-generation method to solve the disassembly sequence-planning problem. The proposed method first generates disassembly subsets based on disassembly precedence relationships. Subsequently, multiple high-level feasible subsets are generated, and some of them are selected to generate higher level subsets using specially designed operators. After several iterations, different optimal disassembly sequences can be obtained. The principal merit of this method is that high-quality disassembly sequences can be generated in the solution construction process. Numerical experiments were conducted by applying the proposed method to three case studies, and the performance of the proposed method for finding optimal solutions and reducing computational time was evaluated in comparison with state-of-the-art methods. The parameters employed in this method were also analysed to explain the mechanism adopted.

关键词： Disassembly sequence planning disassembly subsets precedence knowledge firework algorithm

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Multi-task offloading scheme for UAV-enabled fog computing networks

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-16页

作者： Li, Xujie Zhou, Lingjie Sun, Ying Ulziinyam, Buyankhishig Hohai Univ Coll Comp & Informat Engn Nanjing 210098 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China Mongolian Univ Sci & Technol Sch Informat & Commun Technol Ulaanbaatar 14191 Mongolia

In unmanned aerial vehicle (UAV)-enabled fog computing networks, how to efficiently offload multiple tasks to the computing nodes is a challenging combinatorial optimization problem. In this paper, in order to optimize the total delay for the UAV-enabled fog computing networks, a simple scheduling algorithm and a multi-task offloading scheme based on fireworks algorithm (FWA) are proposed. First, the system model of multiple tasks offloading in UAV-enabled fog computing networks is described in detail. Then, a simple scheduling algorithm is proposed to optimize the delay of the tasks allocated to a single node. Based on the scheduling algorithm, a multi-task offloading scheme for all tasks and all computing nodes is presented. Finally, simulation results show that the performance of a proposed scheduling algorithm and offloading strategy outperforms than that of a genetic algorithm and a random algorithm. This result can provide an effective optimization for multi-task offloading in UAV-enabled fog computing networks.

关键词： UAV Fog computing networks Task offloading firework algorithm

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A novel mobile charging planning method based on swarm reinforcement learning in wireless sensor networks

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2023年第3期41卷 176-188页

作者： Lyu, Zengwei Li, Pengfei Wei, Zhenchun Xu, Juan Shi, Lei Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

In order to solve the problem of energy supplement in large-scale wireless sensor networks (WSNs), this paper investigates the charging planning problem by introduced multiple wireless charger equipment (WCE). We first established the optimisation model of the multi-WCE charging planning problem to minimise the total charging time and the total energy consumption of the WCE. Then, the problem is modelled as a reinforcement learning process, and the time step, state space, action space, state transfer function and reward function are designed. Moreover, based on the idea of swarm intelligence optimisation method, a multi-learners' strategy is introduced to enable multi-learners to parallel learning, so as to accelerate the solution finding speed. Therefore, a discrete firework Q-learning algorithm is proposed to solve the problem. Experiments show that the proposed algorithm outperforms the baseline algorithms in different network scales.

关键词： wireless sensor network WSN mobile charging planning reinforcement learning firework algorithm

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Application of the BP neural network algorithm to the ultimate bearing capacity test of building structures

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第3期24卷 1759-1770页

作者： Yan, Chen Hohai Univ Coll Civil & Transportat Engn Nanjing Jiangsu Peoples R China

The ultimate bearing capacity test effect directly influences the safety performance of the design of building structures. To enhance the safety of building structures, applying the BP neural network algorithm to their ultimate bearing capacity test is studied to improve the test effect. The shear wave velocity of the building structure during stress is collected using the static probing technique. The input samples of the BP neural network are the building structure's shear wave velocity and construction parameters. They are processed by dimensionality reduction through principal component analysis. The firework algorithm is used to optimize the weight of the BP neural network. An early termination training method is designed, and the optimal weight is combined to train the BP neural network. After training, the samples are input after dimensionality reduction, and the building structure's ultimate bearing capacity test results are output. Experimental results show that this method can effectively collect the shear wave velocity of building structures and complete the dimensionality reduction of samples. Under different coaxial stresses, this method can effectively measure the ultimate bearing capacity, about 3800 kN. After parameter optimization, the test value of this method is very close to the target value;that is, the ultimate bearing capacity test precision is high.

关键词： BP neural network architectural results ultimate bearing capacity test application static probing technology firework algorithm

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fireworks Explosion Can Solve the Set Covering Problem 5th

Fireworks Explosion Can Solve the Set Covering Problem

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5th Computer Science On-line Conference (CSOC)

作者： Crawford, Broderick Soto, Ricardo Astudillo, Gonzalo Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Cent Chile Santiago Chile Univ San Sebastian Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru

ISBN: (纸本)9783319336251;9783319336237

The Set Covering Problem is a formal model for many practical optimization problems. It consists in finding a subset of columns in a zero/one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we will use a metaheuristic called fireworks algorithm (FWA) inspired by the fireworks explosion. Through the observation of the way that fireworks explode is much similar to the way that an individual searches the optimal solution in swarm. fireworks algorithm consists of four parts, i.e., the explosion operator, the mutation operator, the mapping rule and selection strategy.

关键词： firework algorithm Set Covering Problem Metaheuristic

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