Evolutionary computation for resource leveling optimization in project management

Kyriklidis, Christos; Dounias, Georgios

doi:10.3233/ica-150508

Cited by 39 publications

(24 citation statements)

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“…Recently, swarm intelligence has paid more attention in academia. 10 The typical swarm intelligent algorithms include artificial colony ant, 11 cuckoo search algorithm, 12 artificial bee colony, 13,14 bat algorithm, [15][16][17] and so on, which have widely used to all kinds of fields such as continuous optimization, [18][19][20] engineering optimization, [21][22][23][24] project management, 25 system optimization, 26 software design, 27,28 and so on. In addition, swarm intelligences have also applied to multi-objective problems.…”

Section: Andmentioning

confidence: 99%

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Jiang

Chen

et al. 2019

Concurrency and Computation

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Summary The ant colony labor division model can be used to solve the dynamic and variable traffic signal timing problem because of its adaptive and self‐adjusting characteristics. Based on the basic model, this paper proposes a new extended ant colony labor division model for traffic signal timing. This model is combined with the vehicle characteristics to modify the calculation method of environmental stimulus values. Using the vehicle delay in the unit period, the original fixed response threshold is modified to the dynamic response threshold, and the state transition equation is reconstructed. Based on the mixed traffic flow model of two‐phase single‐point intersection cellular automata, through comparative experiments and discussion and analysis, it is found that the extended ant colony labor division model can effectively improve road traffic capacity according to road conditions and reasonable traffic signal timing.

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Section: Andmentioning

confidence: 99%

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Jiang

Chen

et al. 2019

Concurrency and Computation

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“…The development of the GAs has provided another approach for adjusting the parameters in the design of controllers (Kyriklidis & Dounias, ). The GA is often preferred over gradient‐based optimization methods because it uses the crossover and mutation operations to search in multiple directions thus avoiding entrapment in a local optimum (Rostami & Neri, ; Wright & Jordanov, ).…”

Section: Intelligent Controlmentioning

confidence: 99%

“…The development of the GAs has provided another approach for adjusting the parameters in the design of controllers (Kyriklidis & Dounias, 2016).…”

Section: Ga-based Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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“…Optimization techniques have been used widely for the solution of many engineering and real‐life problems such as the allocation of resources (Kyriklidis & Dounias, ), product design and development (Lostado, Fernandez, Mac Donald, & Villanueva, ; Paz, Pei, Monzón, Ortega, & Suárez, ), process planning and scheduling (X. X. Li, Li, Cai, & He, ), wind farm distribution network optimization (Cerveira, Baptista, & Solteiro Pires, ), vertical transportation optimization in skyscrapers (Koo, Hong, Yoon, & Jeong, ), railway line design and timetable optimization (Castillo, Grande, Moraga, & Sánchez‐Vizcaíno, ), freeway travel cost optimization (Shahabi, Unnikrishnan, & Boyles, ), sustainable road network design (Y. Wang & Szeto, ), bridge design optimization (Bisadi & Padgett, ), construction scheduling (Karim & Adeli, ), mountain railway alignment optimization (W. Li et al, ), road weather information system network optimization (Kwon, Fu, & Melles, ), cost optimization of concrete (Sirca & Adeli, ) and steel building structures (Tashakori & Adeli, ) and composite floors (Adeli & Kim, ), free‐form steel space‐frame roof design optimization (Kociecki & Adeli, ), freeway work zone traffic delay and cost optimization (Jiang & Adeli, ), optimal control of bridges (Adeli & Saleh, ) and buildings (Saleh & Adeli, ), and among others.…”

Section: Introductionmentioning

confidence: 99%

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

2017

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Recently, multi‐ and many‐objective meta‐heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper‐parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas.

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Evolutionary computation for resource leveling optimization in project management

Cited by 39 publications

References 0 publications

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Control methodologies for vibration control of smart civil and mechanical structures

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

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