•The search of FA is determined by the attractions among fireflies.•Too many attractions may result in the oscillations of search and high complexity.•A neighborhood attraction model is proposed in ...this paper.•The proposed approach can effectively improve the performance of FA.
Firefly algorithm (FA) is a new optimization technique based on swarm intelligence. It simulates the social behavior of fireflies. The search pattern of FA is determined by the attractions among fireflies, whereby a less bright firefly moves toward a brighter firefly. In FA, each firefly can be attracted by all other brighter fireflies in the population. However, too many attractions may result in oscillations during the search process and high computational time complexity. To overcome these problems, we propose a new FA variant called FA with neighborhood attraction (NaFA). In NaFA, each firefly is attracted by other brighter fireflies selected from a predefined neighborhood rather than those from the entire population. Experiments are conducted using several well-known benchmark functions. The results show that the proposed strategy can efficiently improve the accuracy of solutions and reduce the computational time complexity.
Firefly algorithm (FA) is an effective optimization technique based on swarm intelligence, which has been successfully applied to various practical engineering problems. In this paper, a new dynamic ...FA (called NDFA) is proposed for demand estimation of water resources in Nanchang city of China. First, a dynamic parameter strategy is used to avoid manually adjusting the step factor. Second, three estimation models in different forms (linear, exponential and hybrid) are developed in terms of the historical water use and local economic structure. Third, normalization method is utilized to eliminate the influences of different units of data. In the experiments, water use in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 are used for finding the optimal weights of the models, and the rest of data (2013–2015) are applied to test the models. Computational results show that all five FA variants can achieve promising solutions. The proposed NDFA obtains better performance than four other FA variants, and its prediction accuracy is up to 97.91%. Finally, the water demand in Nanchang city from 2017 to 2020 is predicted.
•A novel adaptive hybrid evolutionary firefly algorithm (AHEFA) for shape and size optimization of truss structures under multiple frequency constraints is proposed.•This algorithm is a hybridization ...of the differential evolution (DE) algorithm and the firefly algorithm (FA).•The AHEFA significantly improves the convergence rate and the solution accuracy.•Six numerical examples are examined for the validity of the present algorithm.
This paper presents a novel adaptive hybrid evolutionary firefly algorithm (AHEFA) for shape and size optimization of truss structures under multiple frequency constraints. This algorithm is a hybridization of the differential evolution (DE) algorithm and the firefly algorithm (FA). An automatically adapted parameter is utilized to select an appropriate mutation scheme for an effective trade-off between the global and local search abilities. An elitist technique is applied to the selection phase to choose the best individuals. Accordingly, the convergence rate is significantly improved with the high solution accuracy. Six numerical examples are examined for the validity of the present algorithm.
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•A new multi-objective firefly algorithm is used to solve big optimization problems.•The control parameters are automatically adjusted during the search process.•A crossover strategy ...is utilized to maintain population diversity.•The proposed approach achieves better results than NSGA-II on all test problems.
Multi-objective evolutionary algorithms (MOEAs) have shown good performance on many benchmark and real world multi-objective optimization problems. However, MOEAs may suffer from some difficulties when solving big data optimization problems with thousands of variables. Firefly algorithm (FA) is a new meta-heuristic, which has been proved to be a good optimization tool. In this paper, we present a hybrid multi-objective FA (HMOFA) for big data optimization. A set of big data optimization problems, including six single objective problems and six multi-objective problems, are tested in the experiments. Computational results show that HMOFA achieves promising performance on all test problems.
•FA optimized hybrid fuzzy PIDF controller is proposed for LFC of deregulated power system.•Generation Rate Constraint and Governor Dead Band nonlinearity are considered.•Control parameters of FA are ...tuned by carrying out multiple runs of algorithm.•Superiority of FA is demonstrated by comparing the results with tuned GA.•Results are presented under different contracted scenarios and parameter variation.
In this paper, a novel Firefly Algorithm (FA) optimized hybrid fuzzy PID controller with derivative Filter (PIDF) is proposed for Load Frequency Control (LFC) of multi area multi source system under deregulated environment by considering the physical constraints such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. As the effectiveness of FA depends on algorithm control parameters such as randomization, attractiveness, absorption coefficient and number of fireflies are systematically investigated, the control parameters of FA are tuned by carrying out multiple runs of algorithm for each control parameter variation then the best FA control parameters are suggested. Additionally, the superiority of the FA is demonstrated by comparing the results with tuned Genetic Algorithm (GA). To investigate the effectiveness of the proposed approach, time domain simulations are carried out considering different contracted scenarios and the comparative results are presented. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions. It is observed from the simulation results that the designed controllers are robust and the optimum gains of proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed control scheme is evaluated under random step load disturbance.
•Solar cell models parameters are extracted by Hybrid Firefly algorithm and Pattern Search algorithm (HFAPS).•The proposed HFAPS show high performance in the compare with the other studies ...algorithms.•HFAPS can be used as an effective algorithm to extract parameters of solar cell models.
Accurate estimation the electrical equivalent circuit parameters of photovoltaic arrays of solar cells is needed to enhance the performance of solar energy systems. Thus this field has attracted the attention of various researchers. Since the current versus voltage I-V characteristics of photovoltaic is nonlinear, thus an optimization technique is necessary to adjust experimental data to the solar cell model. Some optimization algorithms have been used to estimate the electrical parameters of the model. However, more investigation is needed to improve estimation of the model. The Firefly algorithm is one of the recently proposed swarm intelligence based optimization algorithm that showed impressive performance in solving optimization problems. This algorithm is good for exploring solution if applied alone but need a local optimization method to improve exploitation. In this study, we combine pattern search as a local optimization method with firefly algorithm to improve this algorithm. The proposed algorithm is applied for parameter estimation of single and double diode solar cell models. To show the performance of this algorithm the results are compared, with the other optimization algorithms for parameters of photovoltaic. The results show that the proposed algorithm is a competitive algorithm to be considered in the modeling of solar cell systems.
This paper reports the development of a maximum power-point tracking (MPPT) method for photovoltaic (PV) systems under partially shaded conditions using firefly algorithm. The major advantages of the ...proposed method are simple computational steps, faster convergence, and its implementation on a low-cost microcontroller. The proposed scheme is studied for two different configurations of PV arrays under partial shaded conditions and its tracking performance is compared with traditional perturb and observe (P&O) method and particle swarm optimization (PSO) method under identical conditions. The improved performance of the algorithm in terms of tracking efficiency and tracking speed is validated through simulation and experimental studies.
This article focuses on the implementation of a hybrid firefly algorithm-particle swarm optimization (FAPSO) scheme for optimizing the parameters of an interval type-2 fractional order fuzzy ...proportional integral derivative (IT2FOFPID)-based power system stabilizer (PSS) to minimize the low-frequency oscillations in a power system. Here, the IT2FOFPID-based PSS is designed by considering speed deviation and acceleration as input signals. In this article, a single machine infinite bus system and the New England 10 machine 39-bus power system are used for testing and comparing the approaches. Stability studies are also performed using OPAL-RT's OP5600, a real-time digital simulator. The comparative studies demonstrate that the hybrid FAPSO optimized IT2FOFPID-PSS provides better damping and stability performance when compared with the PSSs based on the FA/PSO/ hybrid genetic algorithm and bacterial foraging optimization and hybrid differential evolution and pattern search optimized IT2FOFPID approaches under various operating scenarios.
Firefly algorithm (FA) is a new swarm intelligence optimization method, which has shown good search abilities on many optimization problems. However, the performance of FA highly depends on its ...control parameters. In this paper, we investigate the control parameters of FA, and propose a modified FA called FA with adaptive control parameters (ApFA). To verify the performance of ApFA, experiments are conducted on a set of well-known benchmark problems. Results show that the ApFA outperforms the standard FA and five other recently proposed FA variants.
This study adopted a metaheuristic approach based on the firefly algorithm (FA) optimization to generate an appropriate configuration for an 8 × 8 Substitution boxes (S-boxes). The FA can construct a ...strong S-box that satisfies the stipulated criteria by rapidly searching for the optimal or near-optimal feature subsets that minimize a given fitness function. The FA is a newly developed computation technique inspired by fireflies and their flash lighting process. However, FA may suffer from premature convergence when solving optimization problems. This study proposes a new FA modification, namely, globalized firefly algorithm (GFA), which employs random movement based on the best firefly using chaotic maps. The best firefly does not conduct any search in the standard firefly algorithm (SFA). The proposed algorithm utilizes a new design for retrieving strong S-boxes based on SFA and GFA.
Bijectivity, strict avalanche criteria, nonlinearity, input/output XOR distribution, bit independence criteria, and linear probability were also analyzed. The result was compared with a few previous S-box generation methods. Overall, the experimental outcome revealed that the design of the proposed S-boxes has satisfactory cryptographic characteristics.
