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A discrete modified fireworks algorithm for community detection in complex networks

APPLIED INTELLIGENCE 2017年第2期46卷 373-385页

作者： Guendouz, Mohamed Amine, Abdelmalek Hamou, Reda Mohamed Tahar Moulay Univ Saida GeCoDe Lab Saida Algeria

In the last decade, several algorithms have been proposed to solve the problem of community detection in complex networks. Many of them are based on swarm intelligence and evolutionary algorithms. Most of these algorithms use the modularity density as a fitness function to maximize. However, these algorithms attempt to find the best solution without taking into consideration the structure of the network. In this paper, a new discrete modified fireworks algorithm (FWA) has been developed to solve the problem of community detection. A new initialization strategy and new mutation strategies are proposed, based on the label propagation strategy to enhance the algorithm and to speed up its convergence. The proposed algorithm has been evaluated on real-world and synthetic networks. Experimental results compared with three other known algorithms show the effectiveness of using our proposed algorithm for solving the problem of detecting communities in complex networks.

关键词： Community detection Evolutionary algorithms Swam intelligence fireworks algorithm

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HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networks

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APPLIED INTELLIGENCE 2021年第2期51卷 1118-1132页

作者： Ji, Junzhong Xiao, Hanghang Yang, Cuicui Beijing Univ Technol Fac Informat Technol Beijing Coll Comp Sci Beijing Peoples R China Beijing Artificial Intelligence Inst Municipal Key Lab Multimedia & Intelligent Softwa Beijing Peoples R China

Functional module detection in protein-protein interaction (PPI) network is one important content of the proteomics research in the post-genomic era. Nowadays the swarm intelligence and evolutionary based approaches have become effective ways for detecting functional modules. This paper proposes a novel hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in PPI networks (called HFADE-FMD). HFADE-FMD first initializes each firework individual into a candidate functional module partition based on label propagation according to the topological and functional information between protein nodes. Then HFADE-FMD uses the explosion operator of firework algorithm, and mutation, crossover and selection strategies of differential evolution algorithm to iteratively search for better functional module partitions. To verify the performance of HFADE-FMD, this paper compared it with ten competitive methods on four public PPI datasets. The experimental results show that HFADE-FMD achieves prominent performance with respective to Recall, Sn, PPV, and ACC metrics while performing well in terms of Precision and F-measure metrics. Thus, it is able to more accurately detect functional modules and help biologists to find some novel biological insights.

关键词： Protein-protein interaction network Functional module detection fireworks algorithm Explosion operation Differential evolution strategies

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Improved fireworks algorithm with information exchange for function optimization

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KNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 82-90页

作者： Cheng, Rong Bai, Yanping Zhao, Yu Tan, Xiuhui Xu, Ting North Univ China Sch Sci Taiyuan 030051 Shanxi Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan 030051 Shanxi Peoples R China

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. Optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively. (C) 2018 Elsevier B.V. All rights reserved.

关键词： fireworks algorithm Swarm intelligence Function optimization LQR controller

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Effectiveness of approximation strategy in surrogate-assisted fireworks algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2015年第5期6卷 795-810页

作者： Pei, Yan Zheng, Shaoqiu Tan, Ying Takagi, Hideyuki Univ Aizu Div Comp Sci Aizu Wakamatsu Fukushima 9658580 Japan Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Key Lab Machine PerceptMinist Educ Beijing 100871 Peoples R China Kyushu Univ Fac Design Fukuoka 8158540 Japan

We investigate the effectiveness of approximation strategy in a surrogate-assisted fireworks algorithm, which obtains the elite from approximate fitness landscape to enhance its optimization performance. We study the effectiveness of approximation strategy from the aspects of approximation method, sampling data selection method and sampling size. We discuss and analyse the optimization performance of each method. For the approximation method, we use least square approximation, spline interpolation, Newton interpolation, and support vector regression to approximate fitness landscape of fireworks algorithm in projected lower dimensional, original and higher dimensional search space. With regard to the sampling data selection method, we define three approaches, i.e., best sampling method, distance near the best fitness individual sampling method, and random sampling method to investigate each sampling method's performance. With regard to sample size, this is set as 3, 5, and 10 sampling data in both the approximation method and sampling method. We discuss and compare the optimization performance of each method using statistical tests. The advantages of the fireworks algorithm, a number of open topics, and new discoveries arising from evaluation results, such as multi-production mechanism of the fireworks algorithm, optimization performance of each method, elite rank, interpolation times and extrapolation times of elites are analysed and discussed.

关键词： fireworks algorithm Fitness landscape approximation Elite strategy Surrogate-assisted fireworks algorithm Dimensionality reduction

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An improved fireworks algorithm for the constrained single-row facility layout problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第8期59卷 2309-2327页

作者： Liu, Silu Zhang, Zeqiang Guan, Chao Zhu, Lixia Zhang, Min Guo, Peng Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu Peoples R China

The single-row facility layout problem (SRFLP) decides upon the arrangement of facilities in a straight row so as to minimise the material handling cost. Generally, this problem allows the placement of the facilities in any location in any order without any restriction, but some practical SRFLP cases are required to meet various types of constraints imposed on the placement of its facilities in the engineering of the placement of factory machinery and equipment. According to this new perspective, we studied the constrained SRFLP (cSRFLP) in this paper. In this problem, a few facilities need to be placed in certain positions or specified orders with or without the requirement to be located next to each other. To address this novel layout problem, related constraints are defined and classified, and novel mixed-integer programming models for the problem are formulated. Subsequently, the CPLEX software is employed to solve this problem. Then a constrained improved fireworks algorithm is presented, which is discretised to solve the considered cSRFLP. Our computational experiments demonstrate that the proposed algorithm performs better than the existing algorithms in solving the problem.

关键词： constrained single-row facility layout problem fireworks algorithm mixed-integer programming model facility layout design

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Multi-stage generation and transmission coordinated planning method with a modified fireworks algorithm facing to energy internet

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8985-S8997页

作者： Liu, Dunnan Ouyang, Shaojie Ge, Rui Yang, Mo Zhang, Xiaochun Tan, Fanghui North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Guangdong Power Grid Co Ltd Management Sci Res Inst Guangzhou Guangdong Peoples R China Natl Elect Power Dispatching & Commun Ctr Beijing 100031 Peoples R China North China Elect Power Univ Sch Econ & Management Beijing Peoples R China

The integration of energy internet (EI) and electric power system has a profound influence on electric power system planning. Therefore, this paper mainly resolves the EI-oriented multi-stage generation and transmission coordinated planning problem with a modified fireworks algorithm. Firstly, we combined with Stackelberg game theory and focused on subject game theory, system low carbon characteristic and renewable energy generation, a multi-stage generation and transmission coordinated planning model for electric power system is proposed. The model has a main objective function of maximizing the social welfare and two secondary objective functions of maximizing individual profits in the operation stage and maximizing overall profits of generation companies in the planning stage. Then, a modified fireworks algorithm (MFWA) is proposed with good expandability, convergence and adaptability to solve the problem. The MFWA is modified by introducing Gaussian Mutation to improve global search capability. The basic coding method and solution procedure of the MFWA are depicted. Finally, an improved IEEE-24 bus system is used to test the proposed planning method and MFWA. By comparing three scenarios and analysing results, we can verify the validity and feasibility of the proposed method.

关键词： Energy internet fireworks algorithm Electric power system Coordinated planning

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A Cooperative Framework for fireworks algorithm

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第1期14卷 27-41页

作者： Zheng, Shaoqiu Li, Junzhi Janecek, Andreas Tan, Ying Peking Univ Dept Machine Intelligence Key Lab Machine Percept Minist Educ Nanjing 210007 Jiangsu Peoples R China China Elect Technol Grp Corp Res Inst 28 Nanjing 210007 Jiangsu Peoples R China Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Beijing 100871 Peoples R China Peking Univ Key Lab Machine Percept Minist Educ Beijing 100871 Peoples R China Univ Vienna Res Grp Theory & Applicat Algorithms Vienna Austria

This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that (i) the current selection strategy has the drawback that the contribution of the firework with the best fitness (denoted as core firework) overwhelms the contributions of all other fireworks (non-core fireworks) in the explosion operator, (ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which significantly improves the exploitation capability by using an independent selection method and also increases the exploration capability by incorporating a crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions indicate that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, and the standard particle swarm optimization SPSO2007/SPSO2011 in terms of convergence performance.

关键词： Swarm intelligence fireworks algorithm explosion amplitude cooperative strategy

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A core firework updating information guided dynamic fireworks algorithm for global optimization

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SOFT COMPUTING 2020年第2期24卷 1185-1211页

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

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) exhibits promising performance on a wide set of optimization problems, for which the fireworks algorithm has been concentrated on and investigated by researchers recently. This paper aims to improve the performance of the FWA by exploiting updating information of the core firework to guide the algorithm's searching process. Based on this mentality, this paper ameliorated the explosion strategy of core firework of dynamic fireworks algorithm (dynFWA). The proposed algorithm, named dynPgFWA in this paper, improved FWA from two aspects: amplifying the explosion amplitude on the direction on which core firework is updated, and making more sparks which are generated by core firework distributed on this direction to enhance the algorithm's searching ability on updating direction. A numerical experiment on CEC2015 and CEC2017 test suite was implemented to verify the performance of the proposed algorithm. Results of the experiment indicated that dynPgFWA outperformed the compared evolutionary algorithms in the quality of solutions.

关键词： fireworks algorithm Updating information Core firework Swarm intelligence algorithm Evolutionary computing

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Improved fireworks algorithm with Chaotic Sequence Operator for Large-Scale Non-convex Economic Load Dispatch Problem

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第6期43卷 2919-2929页

作者： Pandey, Vipin Chandra Jadoun, Vinay Kumar Gupta, Nikhil Niazi, K. R. Swarnkar, Anil Malaviya Natl Inst Technol Jaipur Rajasthan India Natl Inst Technol Hamirpur India

fireworks algorithm (FWA) is one of the recently developed meta-heuristic techniques, which has shown potential to solve non-shifted optimization functions. However, several engineering optimization problems have shifted optima where the performance of the algorithm heavily degrades owing to the intrinsic limitations of FWA. This paper proposes an Improved FWA with Chaotic Sequence Operator (IFWA-CSO) to efficiently solve diverse engineering optimization problems having shifted optima. The proposed algorithm is validated on benchmark mathematical functions. The IFWA-CSO algorithm is then proposed to solve complex economic dispatch problem of power systems considering several practical constraints such as valve-point loading effect, ramp rate limits, prohibited operating zones, etc. The application results of the proposed method on 140 generator system are presented and compared with existing methods available in literature.

关键词： fireworks algorithm Chaotic sequence Economic dispatch Prohibited operating zones Ramp rate limits

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A Region Enhanced Discrete Multi-Objective fireworks algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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