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8th International Conference on Swarm Intelligence (ICSI)

作者： Yu, Jun Takagi, Hideyuki Kyushu Univ Grad Sch Design Fukuoka 8158540 Japan Kyushu Univ Fac Design Fukuoka 8158540 Japan

ISBN: (纸本)9783319618241;9783319618234

We propose two strategies for improving the performance of the fireworks algorithm (FWA). The first strategy is to decrease the amplitude of each firework according to the generation, where each firework has the same initial amplitude and decreases in size every generation rather than by dynamic allocation based on its fitness. The second strategy is a local optima-based selection of a firework in the next generation rather than the distance-based selection of the original FWA. We design a set of controlled experiments to evaluate these proposed strategies and run them with 20 benchmark functions in three different dimensions of 2-D, 10-D and 30-D. The experimental results demonstrate that both of the two proposed strategies can significantly improve the performance of the original FWA. The performance of the combination of the two proposed strategies can further improve that of each strategy in almost all cases.

关键词： fireworks algorithm Decrement strategy Local optima-based selection strategy

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Adaptive fireworks algorithm based on simulated annealing 13

Adaptive fireworks algorithm based on simulated annealing

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Ye, Wenwen Wen, Jiechang Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538648223

This paper proposes an adaptive fireworks algorithm based on simulated annealing to solve global optimization problems. It normalizes the individuals before the explosion operation and then restores them after the explosion operation. The subpopulations are produced simultaneously by the explosion operation and the mutation operation, and then a new way to produce the explosion amplitude by introducing the simulated annealing is used to adjust the explosion amplitude adaptively along with the evolution. Experimental results on eight benchmark functions with different shift values show that the proposed algorithm has higher accuracy and faster convergence rate than the other five compared algorithms.

关键词： fireworks algorithm Normalization and reduction Simulated annealing

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Enhanced fireworks algorithm for Dynamic Deployment of Wireless Sensor Networks 2

Enhanced Fireworks Algorithm for Dynamic Deployment of Wirel...

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2nd International Conference on Frontiers of Sensors Technologies (ICFST)

作者： Liu, Xiaolei Zhang, Xiaosong Zhu, Qingxin Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Cyber Secur Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509048601

The optimization for deployment of wireless sensor networks is helpful to improve the sensor coverage and the reliability of system. In this work, an enhanced fireworks algorithm is applied to the dynamic deployment of mobile sensor networks. Compared with the existing mainstream swarm intelligence algorithms, this proposed approach has the advantages of good convergence rate, fast convergence and better coverage, which is closer to the theoretical coverage value. Experimental results show that the enhanced fireworks algorithm is able to get the optimal deployment quickly and effectively in the actual scene.

关键词： component fireworks algorithm wireless sensor networks dynamic deployment

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Elite-Leading fireworks algorithm 8th

Elite-Leading Fireworks Algorithm

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8th International Conference on Swarm Intelligence (ICSI)

作者： Zhao, Xinchao Li, Rui Zuo, Xingquan Tan, Ying Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783319618241;9783319618234

fireworks algorithm (FWA) is effective to solve optimization problems as a swarm intelligence algorithm. In this paper, the elite-leading fireworks algorithm (ELFWA) is proposed based on dynamic search in fireworks algorithm (dynFWA), which is an important improvement of FWA. In dynFWA firework is separated to two group: core-firework (CF) and non-core fireworks (non-CFs). This paper takes some beneficial information from non-CFs to reinforce the local search effect of CF. Random reinitialization and elite-leading operator are used to maintain the diversity of the non-CFs, which play an important role in global search. Based on the CEC2015 benchmark functions suite, ELFWA has a very competitive performance when comparing with state-of-the-art fireworks algorithms, such as dynFWA, dynFWACM and eddynFWA.

关键词： fireworks algorithm Elite-leading operator Random reinitialization

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JPEG Quantization Table Optimization by Guided fireworks algorithm 1

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18th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Tuba, Eva Tuba, Milan Simian, Dana Jovanovic, Raka Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Grad Sch Comp Sci Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania Hamad Bin Khalifa Univ Qatar Environm & Energy Res Inst Doha Qatar

ISBN: (数字)9783319591087

ISBN: (纸本)9783319591087;9783319591070

Digital images are very useful and ubiquitous, however there is a problem with their storage because of their large size and memory requirement. JPEG lossy compression algorithm is prevailing standard that solves that problem. It facilitates different levels of compression (and the corresponding quality) by using recommended quantization tables. It is possible to optimize these tables for better image quality at the same level of compression. This presents a hard combinatorial optimization problem for which stochastic metaheuristics proved to be efficient. In this paper we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for quantization table optimization. We tested the proposed approach on standard benchmark images and compared results with other approaches from literature. By using various image similarity metrics our approach proved to be more successful.

关键词： Image processing JPEG algorithm Quantization tables fireworks algorithm Swarm intelligence

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A Community Detection algorithm Based on Local Double Rings and fireworks algorithm 1

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18th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Ma, TianRen Xia, Zhengyou Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210015 Jiangsu Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

In recent years, more and more algorithms have been proposed to detect communities. An improved community detection algorithm based on the concept of local double rings and the framework of fireworks algorithm (LDRFA) has been proposed in this paper. Inspired by the framework of FWA, an improved distinctive fireworks initialization strategy was given. We use this strategy to obtain a more accurate initial solution. Secondly, on the basis of fireworks algorithm, the amplitude of explosion was used to calculate the probability of changing node label. Thirdly, the mutation operator was proposed. Nodes chose labels based on the idea of LPA. Finally, tests on real-world and synthetic networks were given. The experimental results show that the proposed algorithm has better performance than existing methods in finding community structure.

关键词： Community detection fireworks algorithm Swarm intelligence

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Guided fireworks algorithm Applied to the Maximal Covering Location Problem 8th

Guided Fireworks Algorithm Applied to the Maximal Covering L...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Tuba, Eva Dolicanin, Edin Tuba, Milan Univ Belgrade Fac Math Belgrade Serbia State Univ Novi Pazar Dept Tech Sci Novi Pazar Serbia John Naisbitt Univ Grad Sch Comp Sci Belgrade Serbia

ISBN: (纸本)9783319618241;9783319618234

Maximal covering location problem is an interesting and applicable hard optimization problem. It is a facility location problem where facilities have to be placed in optimal positions to maximize coverage of the weighted demand nodes, while respecting some constraints. This hard optimization problem has been successfully tackled by swarm intelligence algorithms. In this paper we propose adjustment of the recent guided fireworks algorithm for maximal covering location problem. We tested our approach on standard benchmark data sets and compared results with other approaches from literature where our proposed algorithm proved to be more successful, both in the quality of the coverage and convergence speed.

关键词： Maximal covering location problem Optimization Meta-heuristics fireworks algorithm Swarm intelligence

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fireworks algorithm framework for Big Data optimization

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MEMETIC COMPUTING 2016年第4期8卷 333-347页

作者： El Majdouli, Mohamed Amine Rbouh, Ismail Bougrine, Saad El Benani, Bouazza El Imrani, Abdelhakim Ameur Mohammed V Univ Fac Sci Concept & Syst Lab Rabat Morocco Mohammed V Univ Comp Sci Res Lab Fac Sci Rabat Morocco

This paper presents a novel optimization framework based on the fireworks algorithm for Big Data Optimization problems. Indeed, the proposed framework is composed of two optimization algorithms. A single objective fireworks algorithm and a multi-objective fireworks algorithm are proposed for solving the Big Optimization of Signals problem "Big-OPT" which belongs to the Big Data Optimization problems class. The single objective fireworks algorithm adopts a modified search mechanism to ensure rapidity and preserve the explorative capacities of the basic fireworks algorithm. Afterward, the algorithm is extended to handle multi-objective optimization of Big-OPT with a supplementary special sparks phase and a novel strategy for next generation selection. To validate the performance of the framework, extensive tests on six EEG datasets are performed. The framework is also compared with several approaches from recent state of the art. The study concludes the competitive performance of the proposed framework in comparison with the other techniques reported in this paper.

关键词： Big Data Optimization fireworks algorithm Big-OPT EEG Signals Brain Computer Interface

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fireworks algorithm with New Feasibility-Rules in Solving UAV Path Planning 3

Fireworks Algorithm with New Feasibility-Rules in Solving UA...

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3rd International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Alihodzic, Adis Univ Sarajevo Fac Math Sarajevo Bosnia & Herceg

ISBN: (纸本)9781509036967

Unmanned Aerial Vehicle (UAV) path planning is a high dimensional NP-hard problem. It is related to optimizing the flight route subject to various constraints inside the battlefield environments. Since the number of control points is high as well as the number of radars, the traditional methods could not produce acceptable results when tackling this problem. In this paper, we have converted the UAV path planning problem to the constrained one based on new feasibility-rules and then we have implemented the fireworks algorithm (FWA) and applied it later in solving this issue. For experimental purposes, we used the parameters of the battlefield environments from the literature to verify the proposed FWA. The simulation results show that the proposed FWA in all cases outperforms PSO, DE, BA, and CS.

关键词： fireworks algorithm Swarm Intelligence UAV path planning Constrained optimization

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Design of hybrid sliding mode controller based on fireworks algorithm for nonlinear inverted pendulum systems

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ADVANCES IN MECHANICAL ENGINEERING 2017年第1期9卷

作者： Su, Te-Jen Wang, Shih-Ming Li, Tsung-Ying Shih, Sung-Tsun Hoang, Van-Manh Natl Kaohsiung Univ Appl Sci Kaohsiung Taiwan Kaohsiung Med Univ Grad Inst Clin Med Kaohsiung Taiwan Cheng Shiu Univ Dept Elect Engn Kaohsiung Taiwan

The objective of this article is to optimize parameters of a hybrid sliding mode controller based on fireworks algorithm for a nonlinear inverted pendulum system. The proposed controller is a combination of two modified types of the classical sliding mode controller, namely, baseline sliding mode controller and fast output sampling discrete sliding mode controller. The simulation process is carried out with MATLAB/Simulink. The results are compared with a published hybrid method using proportional-integral-derivative and linear quadratic regulator controllers. The simulation results show a better performance of the proposed controller.

关键词： fireworks algorithm baseline sliding mode control fast output sampling inverted pendulum system

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