Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems

Cheng, Min‐Yuan; Tran, Duc-Hoc; Wu, Yuwei

doi:10.1016/j.autcon.2013.10.002

Cited by 74 publications

(27 citation statements)

References 36 publications

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“…This relation did not exist before. These modifications are consistent with the research work by Cheng et al (2014). In order to generate a target schedule, discrete-event simulation-based optimization is applied.…”

Section: Resultsmentioning

confidence: 53%

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Reactive scheduling based on actual logistics data by applying simulation-based optimization

Szczesny

König

2015

Vis. in Eng.

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Section: Resultsmentioning

confidence: 53%

“…The applied configuration values are given in Table 2. The generated target schedule has a make-span of 190 days, which is equal to the results presented in Cheng et al (2014) (cf. Fig.…”

Section: Resultsmentioning

confidence: 93%

Reactive scheduling based on actual logistics data by applying simulation-based optimization

Szczesny

König

2015

Vis. in Eng.

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“…Differential evolution (DE) algorithm, introduced by Storn and Price in 1997, is one of the most popular evolutionary algorithms [26] and may be used in a wide variety of highly nonlinear and complex optimization problems. This algorithm is simply structured and is easy to use, while demonstrating great robustness and fast convergence in solving single-objective global optimization problems [27,28]. Recently, there are several attempts to extend the DE to solve multiobjective problems [29,30], with superior performance to other multiobjective algorithms widely reported and verified [31].…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

“…Calculate CR according to (28) and generate a mutated vector , +1 corresponding to the target vector , according to (27).…”

Section: Selectionmentioning

confidence: 99%

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

Liu

Qin

et al. 2015

International Journal of Distributed Sensor Networks

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Cognitive radio has emerged as a promising solution to address the problems posed by coming spectrum scarcity for the inherently resource-constrained sensor networks. Reliability and energy consumption are key objectives for spectrum sensing in cognitive sensor networks. In this paper, a fast differential evolution algorithm is proposed to optimize the energy consumption and spectrum sensing performance jointly. By constructing a comprehensive performance metric, the joint optimization is transferred to a multiobjective optimal problem, in which the sleeping schedule and censoring mechanism are taken into consideration. The main objective of the proposed algorithm is to minimize the network energy consumption subjected to constraints on the detection performance by optimally deriving the censoring and sleeping probabilities. To accelerate the convergence speed and maintain the diversity, the algorithm utilizes the advantages of opposite-based learning for generating the initial population and a tournament scheme in mutation step. In the crossover step, a control parameters dynamic adjustment scheme is applied to make a trade-off between exploration and exploitation. Finally, a selection mechanism is introduced for generating a well-distributed Pareto optimal front. The simulation results show that the proposed algorithm can reduce the average energy consumption of cognitive sensor node, while improving the global probability of spectrum sensing.

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“…Cheng, Tran and Wu studied the optimization between the duration and the resource. They proposed a new FCDE (the Fuzzy Clustering Chaotic-based Differential Evolution) algorithm to solve complex optimization problems [5]. Ashuri and Tavakolan studied the time-cost-resource optimization (TCRO) problems based on the shuffled frog-leaping model.…”

Section: Introductionmentioning

confidence: 99%

Research on the Schedule Optimization Decision of Construction Project Based on Dominance-Based Rough Set

Xue¹,

Wang²

2015

IJUNESST

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The schedule plan is the important basis of construction project. Because of the uncertain of the construction project environment, the existing schedule plan is very hard to meet the demand of the actual progress, and it is schedule plan optimization decision that is an important progress in the schedule management. The schedule plan optimization decision is a decision-making progress, in which the collaboration among the multi objectives, the constraint conditions, and the collaboration among the multi stakeholders should be considered. In this paper, the dominance-based rough set will be used to make decisions based on characteristics of the schedule plan optimization decision. Through the case study, the decision made by the dominance-based rough set can be understood easily, and improve the Scientific nature in the uncertain construction environment.

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Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems

Cited by 74 publications

References 36 publications

Reactive scheduling based on actual logistics data by applying simulation-based optimization

Reactive scheduling based on actual logistics data by applying simulation-based optimization

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

Research on the Schedule Optimization Decision of Construction Project Based on Dominance-Based Rough Set

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