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A hybrid genetic-firefly algorithm for engineering design problems

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第2期9卷 706-730页

作者： El-Shorbagy, M. A. El-Refaey, Adel M. Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm 32511 Egypt Arab Acad Sci Technol & Maritime Transport Coll Engn & Technol Basic & Appl Sci Dept Smart Village Campus Giza Egypt

firefly algorithm (FA) is a new random swarm search optimization algorithm that is modeled after movement and the mutual attraction of flashing fireflies. The number of fitness comparisons and attractions in the FA varies depending on the attraction model. A large number of attractions can induce search oscillations, while a small number of attractions can cause early convergence and a large number of fitness comparisons that can add to the computational time complexity. This study aims to offer H-GA-FA, a hybrid algorithm that combines two metaheuristic algorithms, the genetic algorithm (GA) and the FA, to overcome the flaws of the FA and combine the benefits of both algorithms to solve engineering design problems (EDPs). In this hybrid system, which blends the concepts of GA and FA, individuals are formed in the new generation not only by GA processes but also by FA mechanisms to prevent falling into local optima, introduce sufficient diversity of the solutions, and make equilibrium between exploration/exploitation trends. On the other hand, to deal with the violation of constraints, a chaotic process was utilized to keep the solutions feasible. The proposed hybrid algorithm H-GA-FA is tested by well-known test problems that contain a set of 17 unconstrained multimodal test functions and 7 constrained benchmark problems, where the results have confirmed the superiority of H-GA-FA overoptimization search methods. Finally, the performance of the H-GA-FA is also investigated on many EDPs. Computational results show that the H-GA-FA algorithm is competitive and better than other optimization algorithms that solve EDPs.

关键词： hybrid algorithms genetic algorithm firefly algorithm engineering design problems

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Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems

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APPLIED SOFT COMPUTING 2022年第0期120卷 108634-108634页

作者： Peng, Hu Xiao, Wenhui Han, Yupeng Jiang, Aiwen Xu, Zhenzhen Li, Mengmeng Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Nowadays, more and more optimization techniques are used to deal with complex engineering optimization problems. firefly algorithm (FA) inspired by the flash communication between fireflies, has been proven to be competitive with other swarm intelligence algorithms and has been widely applied to solve complex engineering optimization problems. However, FA has some defects in dealing with complex engineering optimization problems, such as the exploration and exploitation cannot be well balanced. Therefore, in order to achieve effective performance, the different characteristics of search strategies can be applied at different stages of the search process to achieve a balance between exploration and exploitation. In this paper, a multi-strategy firefly algorithm with selective ensemble (MSEFA) is proposed. In MSEFA, the algorithm has three novel search strategies with different characteristics in the strategy pool. In addition, an idea of selective ensemble is adopted to design a priority roulette selection method. The method can select suitable search strategies in different search stages and coordinate the balance of strategies so that better results can be obtained. Furthermore, a parameter adaptive transformation mechanism is designed to control the decreasing rate of step size alpha. To verify the effectiveness of MSEFA, performance tests are conducted on the CEC 2013 and CEC 2019 test suites, after which MSEFA is used to solve four complex engineering optimization problems. Experimental results show that MSEFA has the best performance compared with other FA variants and other improved swarm intelligence algorithms. In addition, MSEFA also achieves the best results in dealing with four complex engineering optimization problems. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence algorithm firefly algorithm Multi-strategy Selective ensemble Complex engineering optimization problems

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Optimal Charging Scheduling of Electric Vehicle Considering Minimum Power Loss using firefly algorithm 3

Optimal Charging Scheduling of Electric Vehicle Considering ...

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IEEE 3rd International Conference on Power Engineering Applications (ICPEA) - Shaping Sustainability Through Power Engineering Innovation

作者： Mohamad, H. Razali, N. M. Shah, K. I. K. Salim, N. A. Naidu, K. Univ Teknol MARA UiTM Coll Engn Sch Elect Engn Power Syst Planning & Operat Res Grp PoSPO Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9781665475013

With growing concerns about environmental pollution, carbon dioxide emissions, and reliance on fossil fuels energy, electric vehicles have received great attention due to clean and reliable energy supply worldwide. However, random charging of electric vehicles may lower the power system efficiency and affects its total stability. This paper presents an optimal coordinated charging of electric vehicles to achieve minimum power loss using a computational technique i.e., the firefly algorithm. The proposed algorithm considered some constraints such as the system's maximum load demand and voltage profile range and is tested on the IEEE 33-bus radial distribution network. The results are compared between the base load, uncoordinated and coordinated charging. The coordinated charging scheduling is further analyzed by varying the charging rate and observe its impact on the test network. The coordinated charging scheduling is also carried out using Evolutionary Programming and the convergence performance is compared to the proposed firefly algorithm. Results obtained show that the proposed algorithm achieves the optimization purpose by minimizing the power loss under a set of constraints.

关键词： electric vehicle charging scheduling distribution network firefly algorithm minimum power loss

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Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

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PEERJ COMPUTER SCIENCE 2022年 8卷 e956页

作者： Zivkovic, Miodrag Tair, Milan Venkatachalam, K. Bacanin, Nebojsa Hubalovsky, Stepan Trojovsky, Pavel Singidunum Univ Belgrade Serbia Univ Hradec Kralove Dept Appl Cybernet Fac Sci Hradec Kralove Czech Republic Univ Hradec Kralove Dept Math Fac Sci Hradec Kralove Czech Republic

The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

关键词： firefly algorithm Machine learning Benchmark Intrusion detection Optimisation

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A parallel compact firefly algorithm for the control of variable pitch wind turbine

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期111卷

作者： Shan, Jie Chu, Shu-Chuan Weng, Shao-Wei Pan, Jeng-Shyang Jiang, Shi-Jie Zheng, Shi-Guang Fujian Univ Technol Sch Elect Engn & Phys Fuzhou 350000 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Flinders Univ S Australia Coll Sci & Engn 1284 South Rd Clovelly Pk SA 5042 Australia Shanghai Univ Elect Power Sch Elect Power Engn Shanghai 200090 Peoples R China

firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by the flickering behavior of fireflies. Due to its excellent performance, it has been widely used in engineering field. However, FA is a population-based algorithm, which needs to occupy a lot of running memory. It is adverse for some small wind turbines or scenarios with limited memory Therefore, this paper proposes an improved firefly algorithm, called parallel compact firefly algorithm (PCFA). The compact technique helps FA save operation memory, which is advantageous for some usage scenarios limited memory. And the parallel technique helps compact FA achieve better solutions and faster convergence. The proposed PCFA was tested on 28 benchmark functions and applied to the proportional-integral-derivative (PID) parameter tuning of the variable pitch wind turbine. Results demonstrate that (1) PCFA is superior to common compact optimization algorithms not only on less memory consumption, but also on more competitive solutions and faster convergence. (2) PCFA shows the better applicability in the PID parameter tuning of variable pitch wind turbine. It can availably smooth the power output of wind turbine under less memory consumption.

关键词： firefly algorithm Compact technique Parallel technique Communication strategies Variable pitch wind turbine

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Advanced discrete firefly algorithm with adaptive mutation-based neighborhood search for scheduling unrelated parallel machines with sequence-dependent setup times

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第8期37卷 4612-4653页

作者： Ezugwu, Absalom E. Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward AvePietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

The unrelated parallel machine scheduling problem with sequence-dependent setup times is addressed in this paper with the objective of minimizing the elapsed time between the start and finish of a sequence of operations in a set of unrelated machines. The machines are considered unrelated because the processing speed is dependent on the job being executed and not on the individual machines. Generally, the problem is considered NP-hard, as it presents additional complexity to find an optimal solution in terms of minimum makespan. An advanced firefly metaheuristic optimization algorithm is introduced to solve this problem. The proposed method, called the FAII algorithm, aims to improve the standard firefly algorithm's performance by incorporating an enhanced global best solution update mechanism and adaptive mutation-based local and global neighborhood search scheme to improve the quality of the proposed algorithm's generated solution. Several experiments were conducted to compare and validate the proposed algorithms' performance on small and large-scale benchmarked problem instances with up to 12 machines and 120 job combinations. Moreover, the performance of the FAII was also compared with eight other metaheuristic algorithms, which were implemented in parallel with the FAII method. Furthermore, the numerical results of the FAII algorithm were compared with the scheduling results of six other well-known metaheuristics from the literature. The comparison results backed with a comprehensive statistical analysis showed the superiority of the enhanced FA-style scheduling optimization over other metaheuristic methods to find good quality solutions or minimum average makespan.

关键词： firefly algorithm global best local search metaheuristics mutation operators scheduling unrelated parallel machines

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Optimal reconfiguration of balanced and unbalanced distribution systems using firefly algorithm

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2022年第3期23卷 317-328页

作者： Battula, Amrutha Raju Vuddanti, Sandeep Natl Inst Technol Andhra Pradesh Elect Engn Tadepalligudam India

With increasing demand in energy, efficiency, reliability, and resiliency in the power systems, integration of distributed generation (DG) or renewable energy sources in the local distribution system is trending nowadays. The power system optimization in distribution networks plays a prominent role in planning reliable supply to the users. This paper presents a meta-heuristic approach to improve the system performance by considering: a) voltage profile improvement, b) minimize the active power loss, and c) overall operation cost as the objective functions of the distribution system reconfiguration (DSR). Modified Newton Raphson (MNR) is used for a load flow analysis of the 33 bus balanced test system and 25 bus unbalanced distribution system. To study the performance of the system objective functions, three different scenarios are considered: i) with DG integrated, ii) with DSR performed, and iii) DG placed along with DSR. These scenarios are compared with the base system configuration parameters along with convergence comparison of harmony search algorithm (HAS) and particle swarm optimization (PSO) with firefly algorithm (FFA) of the distribution systems.

关键词： cost minimization distribution system reconfiguration firefly algorithm loss minimization voltage stability index

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A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems

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JOURNAL OF SUPERCOMPUTING 2022年第3期78卷 3998-4031页

作者： Goldanloo, Mina Javanmard Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach;two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions.

关键词： Symbiotic organisms search firefly algorithm Continuous optimization problem Optimization

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A new firefly algorithm with mean condition partial attraction

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APPLIED INTELLIGENCE 2022年第4期52卷 4418-4431页

作者： Xu, Guang-Hui Zhang, Ting-Wei Lai, Qiang Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China

As compared with other optimization algorithms (e.g., genetic algorithm, ant colony algorithm, and particle swarm algorithm), FA is relatively simple to be realized. It does not require strict continuous and differentiable conditions, requires less prior knowledge. However, it still cannot effectively avoid slow convergence and poor stability. To optimize FA for the attraction model, a new FA with mean condition partial attraction is proposed (mcFA) in this paper. McFA, characterized by fast computing power, high precision, and easy implementation, is capable of remedying the defect that the FA is easy to converge slowly. As opposed to standard FA, mcFA has determined excellent model parameter values, and the mean condition partial attraction model is more suitable for different dimensional solutions than the full attraction model. Lastly, as verified by the theoretical and experimental results, mcFA outperforms other algorithms on most of the test functions. Moreover, the mean condition partial attraction model is shown to yield better solutions than the full attraction model.

关键词： firefly algorithm Mean condition Partial attraction model Model parameters

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An improved firefly algorithm for numerical optimization problems and it's application in constrained optimization

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3793-3813页

作者： Rezaei, Kamran Rezaei, Hassan Univ Sistan & Baluchestan Fac Math Stat & Comp Sci Dept Comp Sci Zahedan Iran

Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications. Recently, a new and efficient version of this algorithm was introduced as NEFA, which indicated a good performance in solving optimization problems. However, the introduced attraction model in this algorithm may not provide good coverage of the search space and thus trap the algorithm in a local optimum. In this paper, a new and efficient improved firefly algorithm called INEFA is proposed to improve the performance of NEFA. In INEFA, a new model of attraction is introduced in which each firefly can be attracted to brighter fireflies located in different areas of the search space, using the clustering concept to classify fireflies. To evaluate the performance of INEFA, it was used to optimize several known benchmark functions. The results were compared with the results of the firefly algorithm and some of its known improvements. The comparison of results indicated the significant power of INEFA compared to the algorithms. It was used to evaluate its application in solving a constrained optimization problem. The comparison results showed that INEFA performs better than most of the compared algorithms.

关键词： firefly algorithm Attraction model Clustering Mutation

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