Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization

Afshar, Abbas; Ziaraty, A. Kasaeian; Kaveh, A.; Sharifi, Farid

doi:10.1061/(asce)0733-9364(2009)135:7(668)

Cited by 76 publications

(33 citation statements)

References 14 publications

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“…A number of researchers in construction and engineering have adopted ACO to address time-cost trade-off problems (eg, Ng and Zhang, 2008;Afshar et al, 2009;Lakshminarayanan et al, 2010). The ACO algorithm consists of four elements:…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

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A review of methods and algorithms for optimizing construction scheduling

Zhou

Love

Wang

et al. 2013

Journal of the Operational Research Society

115

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Optimizing construction project scheduling has received a considerable amount of attention over the past 20 years. As a result, a plethora of methods and algorithms have been developed to address specific scenarios or problems. A review of the methods and algorithms that have been developed to examine the area of construction schedule optimization (CSO) is undertaken. The developed algorithms for solving the CSO problem can be classified into three methods: mathematical, heuristic and metaheuristic. The application of these methods to various scheduling problems is discussed and implications for future research are identified.

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Section: Metaheuristic Methodsmentioning

confidence: 99%

“…The 'Pareto front' will be obtained after a predetermined number of iterations. Experimental research conducted by Afshar et al (2009) demonstrates that when the number of non-dominated solutions increases, the proposed method can achieve better solutions than the weighting method adopted by the traditional single colony system.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

A review of methods and algorithms for optimizing construction scheduling

Zhou

Love

Wang

et al. 2013

Journal of the Operational Research Society

115

View full text Add to dashboard Cite

show abstract

“…Some researchers have tried to introduce evolutionary algorithms to find global optima such as genetic algorithm (GA) (Feng et al [6]; Gen and Cheng [21]; Zheng et al [10]; Zheng and Ng [9]; Mendes [16,18] and Parveen and Saha [32]) the particle swarm optimization algorithm (Yang [10]), ant colony optimization (ACO) (Xiong and Kuang [28]; Ng and Zhang [24]; Afshar et al [2]) and harmony search (HS) (Geem [29]). In this paper, the optimal time and cost generated by the GA techniques are compared with those produced by other techniques through some problems obtained from literature.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

Magalhães-Mendes¹

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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“…• Search methods: some researchers have tried to introduce evolutionary algorithms to find global optima such as genetic algorithm (GA) [7,10,28,37,38] the particle swarm optimization algorithm [35], ant colony optimization (ACO) [1,26,34] and harmony search (HS) [9].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Approach for Time-Cost Optimization Using a Multi-mode Hybrid Genetic Algorithm

Magalhães-Mendes

2015

Computational Methods in Applied Sciences

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This paper presents a hybrid genetic algorithm for the time-cost optimization (TCO) problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. In construction projects, time and cost are the most important factors to be considered. In this paper, a new hybrid genetic algorithm is developed for the optimization of the two objectives time and cost. The approach was developed in VBA and applied to test problems reported on the literature and compared with other approaches. The results indicate that this approach could assist decision-makers to obtain good solutions for project duration and total cost.

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Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization

Cited by 76 publications

References 14 publications

A review of methods and algorithms for optimizing construction scheduling

A review of methods and algorithms for optimizing construction scheduling

Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

Multiobjective Approach for Time-Cost Optimization Using a Multi-mode Hybrid Genetic Algorithm

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