A variable step size firefly algorithm for numerical optimization

Yu, Shaoming; Zhu, Shenglong; Ma, Yan; Mao, Demei

doi:10.1016/j.amc.2015.04.065

Cited by 114 publications

(66 citation statements)

References 10 publications

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“…FA has been applied for solving different optimization problems such as OOTGU problem and benchmark optimization functions, and its results have been better than those from other popular methods like PSO, GA, and DE. But the method has not met the demand of dealing with large-scale systems and complicated constraints of OOTGU problem, and many studies have worked on improving it [49][50][51]53]. In fact, FA still suffers from several drawbacks such as premature convergence to local optimum or convergence to near global optimum with high number of iterations [54].…”

Section: Firefly Algorithmsmentioning

confidence: 99%

“…The memetic firefly algorithm (MFA) in [49] has focused on the balance of exploration acting as global search and exploitation acting as local search by using adaptive attractiveness β and adaptive step parameter α with respect to the change of iteration in formula updating new solutions for each lower quality solution. Another improved FA with using adaptive step parameter α (ASPFA) was proposed in [50] by suggesting an adaptive formula for updating step parameter α based on current iteration and the maximum number of iterations. The chaotic firefly algorithm (CFA) was developed in [51] for solving the considered OOTGU problem with different test cases of fuel cost function and constraints.…”

Section: Introductionmentioning

confidence: 99%

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Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

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Section: Firefly Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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“…The objective of the nonlinear ELD optimization problem is to minimize cost while satisfying the load demand and other operational system equality and inequality constraints (Zhu et al, 2015).…”

Section: Problem Formulationmentioning

confidence: 99%

“…In addition, prohibited operating zone, ramp rate limits and multi-fuel options are usually considered. Since the objective is for the solution to converge to superior results in a reduced amount of time putting into consideration system constraints, choosing the appropriate optimization approach is important (Zhu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Modifications include and not restricted to: adding memory, varying parameters of the algorithm throughout the iterations and altering the updating formula for the fireflies. New variants of FFA were proposed and implemented to solve benchmark functions, engineering design problems, economic load dispatch problems and so on (Kazemzadah-Parsi, 2014;FisterJr et al, 2012, Yu et al, 2015Farhani et al, 2011;Gandomi et al, 2013;Yu et al, 2014). Some of these new versions of firefly algorithm recently developed, but not yet applied to solve the ELD problem like modified firefly algorithm(MFA), memeticfirefly algorithm (MFFA) and variable step size firefly algorithm (VSSFA) (Kazemzadah-Parsi, 2014;FisterJr et al, 2012, Yu et al, 2015.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the performance of different firefly algorithms to the economic load dispatch problem in electrical power systems

Moustafa¹,

Badra²,

Abdelaziz³

2017

Int. J. Eng. Sci. Tech

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The planning, operation and control of electric power systems has attracted the attention of many researchers. Thus, effort is put in improving the efficiency of generation and operation of power plants. Economic load dispatch (ELD) is crucial since it is required to schedule committed generating units so as to meet load demand at minimum operating cost. In addition to satisfying all system equality and inequality constraints as well as limitations imposed on the generating units during operation. To solve the economic load dispatch problem, traditional and intelligent techniques were applied. Researchers have shown interest in utilizing metaheuristic methods to solve complex optimization problems in real life applications. In this paper, three alternatives of firefly algorithms are applied to solve the nonlinear ELD problem. A comparative study is carried out on the solution of ELD problem using those recent variants and the classical firefly algorithm for different test cases. Efficiency is evaluated by comparing best solutions obtained in terms of execution time, fuel cost and power loss.

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