NIDE: A Novel Improved Differential Evolution for Construction Project Crashing Optimization

Hoang, Nhat‐Duc

doi:10.1155/2014/136397

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…It is observable that the DeLOCP with the mutation strategy of DE/Target-to-Best/1 and Hybrid DE/Rand/1 and DE/Best/1 have produced the best solution: fitness function = 3054 (with average labor demand = 13.8, maximum labor demand = 16.0, minimum labor demand = 12, and project duration = 32.0 (shift)). The optimized crew sizes and start times of all activities are [13,7,4,9,6,4,6,7,8,8,13] and [1,2,2,6,14,9,9,25,27,17,32], respectively. The optimized daily labor demand is illustrated in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…Brest et al [24], Zhang and Sanderson [25], Qin et al [26], and Zheng et al [20] presented self-adaptive versions of DE in which novel mechanisms of parameter setting are utilized. Hoang [27] introduced a probabilistic similarity-based selection operator that can enhance the DE's selection process. Rahnamayan et al [28] put forward an opposition-based DE (ODE) which exploits the concept of opposition-based learning for population initialization and generation jumping.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Hoang

Nguyen

Pham

2015

Advances in Civil Engineering

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In construction management, the task of planning project schedules with consideration of labor utilization is very crucial. However, the commonly used critical path method (CPM) does not inherently take into account this issue. Consequently, the labor utilization of the project schedule derived from the CPM method often has substantial low ebbs and high peaks. This research proposes a model to obtain project schedule with the least fluctuation in labor demand while still satisfying the project deadline and maintain the project cost. The Differential Evolution (DE), a fast and efficient metaheuristic, is employed to search for the most desirable solution of project execution among numerous combinations of activities’ crew sizes and start times. Furthermore, seven DE’s mutation strategies have also been employed for solving the optimization at hand. Experiment results point out that theTarget-to-Best 1and a new hybrid mutation strategy can attain the best solution of project schedule with the least fluctuation in labor demand. Accordingly, the proposed framework can be an effective tool to assist decision-makers in the project planning phase.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Hoang

Nguyen

Pham

2015

Advances in Civil Engineering

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“…Simultaneously, the calculation and determination of the structural internal forces are performed according to the arithmetic interval operations [10] and the interval optimization algorithm [11,12]. Specifically, the study determines the required internal force results of concrete beams placed on an elastic foundation, applying the hybrid mutant differential evolutionary (HMDE) optimization algorithm [13] combined with the finite-element method (FEM) to compute the interval function. From there, the author determines the range of displacement and internal force results in the beams programmed by the author on Maple.17.…”

Section: Figure 1 Beam On Elastic Foundation -Winkler Ground Modelmentioning

confidence: 99%

Internal force analysis of beams on elastic foundation using differential evolutionary optimization algorithm based on hybrid mutation technique

Duy¹,

Hoa²,

Loan³

2023

UD-JST

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The paper presents the Finite element method (FEM) that calculates the internal force and the displacement of beams on elastic foundation in the case of the uncertainty input parameters described in terms of the number intervals. Using the interval function optimization algorithm combined with the FEM to determine the internal force values of the span reinforced concrete beam structure. This study applies the hybrid differential evolutionary optimization algorithm combined with FEM interval functions to determine the required internal force results of reinforced concrete beams placed on an elastic foundation. The calculation process is programmed using Maple.17 software to determine the displacement output and the resulting internal force in the beam. To check the correctness of the program calculated on Maple.17, the input declared dataset corresponds to the central values of the interval numbers and is recalculated by SAP2000v21 software, then evaluates the results error of internal force on the beam under consideration.

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“…Concerning the selection operator, Hoang [57] introduced a new probabilistic similarity-based method to improve the DE's selection process, and preserve the population diversity. Wang and Gao [58] designed a local selection operator by decomposing the high dimensional problem into some subcomponents and assigning a local fitness function to evaluate each subcomponent.…”

Section: B Modifications Of the Mutation Strategy Crossover Scheme And Selection Operatormentioning

confidence: 99%

Adaptive Differential Evolution Based on Successful Experience Information

Cheng

Wang

et al. 2020

IEEE Access

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As a powerful optimization algorithm for solving nonlinear, complex and tough global optimization problems, differential evolution (DE) has been widely applied in various science and engineering fields. In this paper, considering that the evolution direction of each individual is not fully exploited to guide the search process in most DE algorithms, a new DE variant ( named ADEwSE), which incorporates the successful experience of evolved individuals into classic "current-to-pbest/1" mutation strategy to reduce the randomness of search direction, is proposed. Moreover, crossover matrix sorting scheme based on real crossover rate, opposition learning of crossover rate and adaptive adjustment of top p% values are combined with the new mutation strategy to improve the global search ability. In addition, to improve the searching ability of ADEwSE further, an ADEwSE variant by introducing the linear reduction of population size is proposed. In order to verify and analyze the performance of ADEwSE, numerical experiments on a set of 29 test problems from CEC2017 benchmark for 30, 50 and 100 dimensions are executed. And the experimental results are compared with that of 21 state-of-art DE-based algorithms. Comparative analysis indicates that the ADEwSE and its improved version are competitive with these stateof-art DE variants in terms of solution quality obtained.

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NIDE: A Novel Improved Differential Evolution for Construction Project Crashing Optimization

Cited by 7 publications

References 26 publications

Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Internal force analysis of beams on elastic foundation using differential evolutionary optimization algorithm based on hybrid mutation technique

Adaptive Differential Evolution Based on Successful Experience Information

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