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Research on Intelligent Minefield Attack Decision Based on Adaptive fireworks algorithm

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019年第3期44卷 2487-2496页

作者： Ma Yan Zhao Handong Zhang Wei North Univ China Taiyuan 030051 Shanxi Peoples R China Naral Acad Armanent Beijing 102401 Peoples R China

The decision of intelligent minefield attacking tank forces is a complex multi-constraint and multi-objective nonlinear optimization problem. Aiming at the common defects of commonly used intelligent algorithms and combining with the characteristics of fireworks algorithm, this paper proposed an adaptive fireworks algorithm to deal with it. In this paper, we first established the mathematical model of this problem and transformed the model into an unconstrained single-objective extremum by using the external penalty function method. Furthermore, the adaptive fireworks algorithm is used to solve the model. In order to verify the superiority of adaptive fireworks algorithm to deal with this problem, experimental results show that the adaptive fireworks algorithm has faster convergence speed and shorter computation time than the other algorithms, and the results can intuitively describe the reasonable task allocation scheme of the complex situation, which provides a foundation for studying the force control.

关键词： Intelligent minefield External penalty function method fireworks algorithm Multi-constrained and multi-objective optimization

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The Artificial Fish Swarm algorithm Improved by fireworks algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2022年第4期56卷 311-323页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Li, Hongbo Tianjin Univ Commerce Informat Engn Coll Tianjin 300134 Peoples R China Tianjin Univ Commerce Econ Coll Tianjin 300134 Peoples R China

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.

关键词： artificial fish swarm algorithm fireworks algorithm FWA-artificial fish swarm algorithm function optimization

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Joint optimisation of feature selection and SVM parameters based on an improved fireworks algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2023年第6期26卷 702-714页

作者： Shen, Xiaoning Xu, Jiyong Mao, Mingjian Lu, Jiaqi Song, Liyan Wang, Qian Nanjing Univ Informat Sci & Technol Sch Automat B DAT CICAEET Nanjing 210044 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

In order to reduce the redundant features and improve the accuracy in classification, an improved fireworks algorithm for joint optimisation of feature selection and SVM parameters is proposed. A new fitness evaluation method is designed, which can adjust the punishment degree adaptively with the increase of the number of selected features. A differential mutation operator is introduced to enhance the information interaction among fireworks and improve the local search ability of the fireworks algorithm. A fitness-based roulette wheel selection strategy is proposed to reduce the computational complexity of the selection operator. Three groups of comparisons on 14 UCI classification datasets with increasing scales validate the effectiveness of our strategies and the significance of joint optimisation. Experimental results show that the proposed algorithm can obtain a higher accuracy in classification with fewer features.

关键词： fireworks algorithm support vector machines feature selection parameter optimisation joint optimisation

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Training of neural network for pattern classification using fireworks algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第1期9卷 208-215页

作者： Bolaji, Asaju La'aro Ahmad, Aminu Ali Shola, Peter Bamidele Fed Univ Wukari Fac Pure & Appl Sci Dept Comp Sci PMB 1020 Wukari Taraba State Nigeria Gombe State Univ Dept Math Fac Sci Gombe Nigeria Univ Ilorin Fac Commun & Informat Sci Dept Comp Sci Ilorin Nigeria

The challenge of training the artificial neural networks (ANNs) which is frequently used for classification purpose has been consistently growing over the last few years, this is probably due to the high dimensional and multi-modal nature of the search space. Nature-inspired metaheuristic algorithms have been successfully employed in the process of weight training of such complex continuous optimization problems. In this paper, a recently proposed fireworks algorithm (FWA) is presented for the training of the parameters of the ANNs. FWA is a class of population-based search method which imitates the explosion process of real fireworks at night. In order to investigate the performance of the proposed method, experiments were conducted on seven benchmark problem instance from the UCI machine learning laboratory and the results obtained by the proposed method are compared with those obtained by krill herd algorithm, harmony search algorithm and genetic algorithm. The results of the evaluation showed superiority of the proposed algorithm in both SSE and training CA and had comparative performance in testing CA and thus it can be concluded that FWA could be adopted as one of the new template algorithm for the training of ANNs.

关键词： fireworks algorithm Nature-inspired algorithms Metaheuristics Pattern classification Neural network training

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A local region enhanced multi-objective fireworks algorithm with subpopulation cooperative selection

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第6期24卷 572-586页

作者： Shen, Xiaoning You, Xuan Huang, Yao Guo, Yinan Nanjing Univ Informat Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China China Univ Min & Technol Beijing Sch Informat & Elect Engn Beijing Peoples R China

A local region enhanced multi-objective fireworks algorithm with subpopulation cooperative selection (LREMOFWA) is proposed for multi-objective optimisation. In LREMOFWA, the ranking based on the non-dominated sorting and the crowding distance is taken as the fitness evaluation indicator. A novel way to calculate the explosion amplitude is designed to enhance the search for the local region. The concept of subpopulation is introduced, and the selection operation is performed by the elites in the archive cooperating with the optimal subpopulation sparks. The differential mutation operator is utilised to deal with the repeated individuals in the fireworks, which prevents the algorithm from falling into the local optimum. The proposed algorithm is compared with five state-of-the-art algorithms on the WFG test functions. Experimental results show that the proposed algorithm has better performance with respect to the searching accuracy and diversity. It is suitable for solving multi-objective function optimisation problems with various complex characteristics.

关键词： multi-objective optimisation fireworks algorithm explosion amplitude cooperative selection differential mutation

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Construction Method and Performance Analysis of Chaotic S-Box Based on fireworks algorithm

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2019年第12期29卷

作者： Wang, Juan Pan, Bowen Tang, Cong Ding, Qun Heilongjiang Univ Elect Engn Inst Harbin 150001 Heilongjiang Peoples R China Heilongjiang Univ Sci & Technol Elect & Informat Engn Inst Harbin 150027 Heilongjiang Peoples R China

How to choose a chaotic sequence to construct a chaotic S-box is a key factor to determine the performance of the S-box. In this paper, a construction method for chaotic S-box based on fireworks algorithm is proposed, in which the fireworks algorithm is used to search for the optimal solution in the solution space of the Lorenz chaotic system. The performances of S-box are tested through the nonlinearity, differential uniformity, strict avalanche criterion, output bits independence criterion and bijectivity, and compared with the existing classical S-boxes based on the Lorenz chaotic system. The results indicate that the S-box in this paper has more excellent cryptographic characteristics, which will provide a new idea for the efficient generation of chaotic S-box.

关键词： S-box Lorenz system chaotic sequence fireworks algorithm

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Dynamic Multi-Swarm Competitive fireworks algorithm for Global Optimization and Engineering Constraint Problems

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第4期31卷 619-648页

作者： Lei, Ke Wu, Yonghong Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China

As a novel intelligent algorithm, fireworks algorithm (FWA) is applied to deal with different types of optimization problems. Since FWA's search processes are relatively simple, it is inefficient. In this paper, a dynamic multi-swarm competitive fireworks algorithm (DMCFWA) is developed to enhance the search capability of FWA. Firstly, based on the scaling coefficient updated by utilizing the fitness value of the optimal firework, the dynamic explosion amplitude strategy is proposed to improve the search capability of the best firework. Secondly, utilizing the location information of the fireworks, an improved search method is designed to enhance the local search capability of firework swarms. Thirdly, a multi-swarm independent selection technique and a restart operation are adopted to boost its abilities of global exploration and local exploitation. Finally, to reduce the computational cost of FWA, a new initialization method is used and a new model for calculating the spark number is embedded in DMCFWA. By adopting these strategies, DMCFWA easily implements and does well in exploitation and exploration. CEC2017 test suite and four engineering constraint problems are used to demonstrate the performance of DMCFWA. Experimental results show that DMCFWA performs more effectively and stably than its competitors.

关键词： Dynamic multi-swarm competitive strategy fireworks algorithm metaheuristic global optimization engineering optimization

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A fireworks algorithm based Gerchberg-Saxton algorithm for the design of diffractive optical element for beam shaping

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OPTICS COMMUNICATIONS 2024年 557卷

作者： Niu, Bowen Dai, Xingang Hu, Yanjun Zhang, Hongru Jing, Gaoshan Zhang, Zhiping Fan, Guofang Beijing Jiaotong Univ Inst Lightwave Technol Key Lab All Opt Network & Adv Telecommun Network Minist Educ Beijing 100044 Peoples R China Chinese Acad Sci Inst Microelect Beijing 100444 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

In this paper, a fireworks algorithm based Gerchberg-Saxton (GS) algorithm for the design of diffractive optical element is developed, named FW-GS algorithm, in which, the explosion mechanism of the fireworks algorithm is introduced to efficiently find the initial phase, thereby ensuring a superior phase solution for the subsequent GS algorithm. By employing this approach to shape a Gaussian beam into a square beam, the results illustrated the notable improvement of 16.10 % in RMSE and 3.29 % in diffractive efficiency when compared to the traditional GS algorithm, while exhibiting stable optimization performance. Furthermore, the proposed algorithm demonstrated adaptability to various shaping targets, with RMSE improved by about 16 % and diffractive efficiency improved by more than 3 % for shaping to circular, annular, and rectangular beams.

关键词： Diffractive optical element Beam shaping fireworks algorithm GS algorithm FW-GS algorithm

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An Improved Enhanced fireworks algorithm Based on Adaptive Explosion Amplitude and Levy Flight

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ENGINEERING LETTERS 2020年第4期28卷 1348-1357页

作者： Qu, Qiang Qi, Mei-li Gong, Ru-bing Chen, Xue-bo Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

To solve the problems of the waste of resources caused by the small explosion amplitude (even close to 0) of the best firework in the conventional fireworks algorithm (FWA), and the relatively weak local search ability caused by the minimal explosion amplitude check method in the enhanced fireworks algorithm (EFWA), this paper proposes two improved strategies for FWA. First, based on the heuristic information of the distance between the best firework and other fireworks, an adaptive explosion amplitude strategy is proposed to search the local area accurately at the final phase of the FWA. Second, the highly random Levy flight strategy is adopted instead of the Gaussian sparks strategy to generate mutation sparks in the EFWA to enhance the diversity of local search. Simulation results on 12 standard benchmark functions and their shifted functions indicate that the proposed algorithm improves the optimization precision and obtains better performance in high-dimensional complex optimization problems compared with the EFWA, the hybrid fireworks algorithm with differential evolution operator (FWADE), and the particle swarm optimization algorithm (PSO).

关键词： enhanced fireworks algorithm fireworks algorithm adaptive explosion amplitude Levy flight

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Dynamic Search fireworks algorithm with Adaptive Parameters

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INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE 2020年第1期11卷 115-135页

作者： Gong, Chibing Guangdong Tech Coll Water Resource & Elect Engn Coll Elect Power Engn Guangzhou Guangdong Peoples R China

As a comparatively new algorithm of swarm intelligence, the dynamic search fireworks algorithm (dynFWA) imitates the explosion procedure of fireworks. With the goal of achieving global optimization and further boosting performance of dynFWA, adaptive parameters are added in this present study, called dynamic search fireworks algorithm with adaptive parameters (dynFWAAP). In this novel dynFWAAP, a self-adaptive method is used to tune the amplification coefficient Ca and the reduction coefficient Cr for fast convergence. To balance exploration and exploitation, the coefficient of amplitude alpha and the coefficient of sparks beta are also adapted, and a new selection operator is proposed. Evaluated on twelve benchmark functions, it is evident from the experimental results that the dynFWAAP significantly outperformed the three variants of fireworks algorithms (FWA) based on solution accuracy and performed best in other four algorithms of swarm intelligence in terms of time cost and solution accuracy.

关键词： fireworks algorithm Global Optimization Parameters Adaption Swarm Intelligence

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