An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures

Ho-Huu, V.; Nguyen-Thoi, Trung; Nguyen-Thoi, M. H.; Le-Anh, L.

doi:10.1016/j.eswa.2015.04.072

Cited by 86 publications

(27 citation statements)

References 26 publications

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“…It is clear that the manner of producing an 7 Complexity updated step size by using (21) is similar to mutation operation of differential evolution algorithm (DEA) in which β acts as mutation factor ranging from 0 to 2. Some studies before [55,56] have pointed out the disadvantages of DEA such as low convergence to global optimum or trapping into local optimum easily. Consequently, the proposed improvement aims to tackle the constrictions of FA by using the following expressions.…”

Section: The Second Proposed Improvementmentioning

confidence: 99%

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: The Second Proposed Improvementmentioning

confidence: 99%

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

2018

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“…198.064 (6) 252.258 (7) 285.161 (8) 363.225 (9) 388.386 (10) 494.193 (11) 506.451 (12) 641.289 (13) 645.160 (14) 729.031 (15) 792.256 (16) 816.773 (17) 939.998 (18) 1008.385 (19) 1045.159 (20) 1161.288 (21) 1283.868 (22) 1374.191 (23) 1535.481 (24) 1690.319 (25) 1696.771 (26) 1858.061 (27) 1890.319 (28) 1993.544 (29) 2180.641 (30) 2238.705 (31) 2290.318 (32) 2341.931 (33) 2477.414 (34) 2496.769 (35) 2503 …”

Section: The 72-bar Spatial Truss Exampleunclassified

“…Clearly, the standard FA cannot be applied directly to deal with size and topology optimization with discrete design variables as their positions are real numbers. There are many different techniques proposed to solve this problem, such as (1) utilizing a binary-coded technique to handle discrete variables (Datta and Figueira 2011 [30], Datta and Figueira 2013 [31]), (2) replacing the continuous variables by the closest discrete variables, (3) transforming the design variable to log 10 and carrying out the algorithm operation on log 10 , (4) representing each discrete variable by an integer in ascending order and performing the algorithm operation on the integer (Ho-Huu et al 2015 [32]), and (5) forming a linking relationship between the integers and discrete variables as in technique (4) In this study, the discrete variables are arranged in ascending order. Represent each discrete variable by an integer in ascending order.…”

Section: Discretization Of Famentioning

confidence: 99%

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Hu³

et al. 2017

Mathematical Problems in Engineering

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Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short) is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

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“…The ICDE is the latest versions of differential evolution (DE) algorithm in order to solve constrained optimization problem [22,25,26]. It includes two main parts, the Improved (µ+λ)-differential evolution (IDE) to search in the design space and the archiving-based adaptive tradeoff model (ArATM) to handle the constraints of the problem.…”

Section: Optimization Algorithm For Reliability Assessmentmentioning

confidence: 99%

Structural Reliability Assessment Based on the Improved Constrained Differential Evolution Algorithm

Zaeimi

Ghoddosian

2018

Period. Polytech. Civil Eng.

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In this work, the reliability analysis is employed to take into account the uncertainties in a structure. Reliability analysis is a tool to compute the probability of failure corresponding to a given failure mode. In this study, one of the most commonly used reliability analysis method namely first order reliability method is used to calculate the probability of failure. Since finding the most probable point (MPP) or design point is a constrained optimization problem, in contrast to all the previous studies based on the penalty function method or the preference of the feasible solutions technique, in this study one of the latest versions of the differential evolution metaheuristic algorithm named improved (μ+λ)-constrained differential evolution (ICDE) based on the multi-objective constraint-handling technique is utilized. The ICDE is very easy to imple-

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An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures

Cited by 86 publications

References 26 publications

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

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

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Structural Reliability Assessment Based on the Improved Constrained Differential Evolution Algorithm

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