Genetic algorithms for the scheduling problem with arbitrary precedence relations and skilled operators

Mencía, Raúl; Sierra, María R.; Mencía, Carlos; Varela, Ramiro

doi:10.3233/ica-160519

Cited by 28 publications

(13 citation statements)

References 33 publications

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“…In their book, Adeli and Hung () advanced adroit integration of ANN (Quintian & Corchado, ; Weissenberger, Meier, Lengler, Einarsson, & Steger, ) and GA (Li, Pu, Schonfeld, Yang, Zhang, Wang, and Xiong, ; Mencıa, Sierra, Mencıa, & Varela, ; Zhao, Guo, Zhou, & Zhang, ) with fuzzy logic (D'Urso, Masi, Zuccaro, & De Gregorio, ; Peng, Wang, Shi, Pérez‐Jiménez, & Riscos‐Núñez, ) for the solution of complex and intractable problems thus establishing the field of CI. Since then many neuro‐fuzzy algorithms have been developed and applied to different pattern recognition problems.…”

Section: Intelligent Controlmentioning

confidence: 99%

“…advanced adroit integration of ANN(Quintian & Corchado, 2017;Weissenberger, Meier, Lengler, Einarsson, & Steger, 2017) and GA(Li, Pu, Schonfeld, Yang, Zhang, Wang, and Xiong, 2017;Mencıa, Sierra, Mencıa, & Varela, 2016;Zhao, Guo, Zhou, & Zhang, 2018) with fuzzy logic(D'Urso, Masi, Zuccaro, & De Gregorio, 2018;Peng, Wang, Shi, Pérez-Jiménez, & Riscos-Núñez, 2017) …”

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confidence: 99%

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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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Section: Intelligent Controlmentioning

confidence: 99%

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confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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“…Min et al [68] presented a two-way liquid damper using a combination of TLCD and Tuned Sloshing Damper on a 64-story building structure. Hejazi et al [69] proposed a multi-objective optimization technique using GA [70,71] to modify the performance of passive devices in order to reduce the damage response of structural members as well as story displacements. Hejazi et al [72] presented a finite element-based analytical model as well as a constitutive law for viscous wall dampers for RC structures subjected to seismic loading.…”

Section: Pvc In Buildingsmentioning

confidence: 99%

Invited Review: Recent developments in vibration control of building and bridge structures

Ghaedi¹,

Ibrahim²,

Adeli³

et al. 2017

J. vibroeng.

109

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This paper presents a state-of-the-art review of recent articles published on active, passive, semi-active and hybrid vibration control systems for structures under dynamic loadings primarily since 2013. Active control systems include active mass dampers, active tuned mass dampers, distributed mass dampers, and active tendon control. Passive systems include tuned mass dampers (TMD), particle TMD, tuned liquid particle damper, tuned liquid column damper (TLCD), eddy-current TMD, tuned mass generator, tuned-inerter dampers, magnetic negative stiffness device, resetting passive stiffness damper, re-entering shape memory alloy damper, viscous wall dampers, viscoelastic dampers, and friction dampers. Semi-active systems include tuned liquid damper with floating roof, resettable variable stiffness TMD, variable friction dampers, semi-active TMD, magnetorheological dampers, leverage-type stiffness controllable mass damper, semi-active friction tendon. Hybrid systems include shape memory alloys-liquid column damper, shape memory alloy-based damper, and TMD-high damping rubber.

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“…Thus, swarm and evolutionary computations based algorithms to solve the optimization problem are required. There are abundant applications of swarm and evolutionary computations in civil engineering, such as Genetic Algorithm (Han and Kim, ; Jiang and Adeli, ; Sgambi et al., ; Kociecki and Adeli, ; Cha and Buyukozturk, ; Mencia et al., ), Ant Colony Algorithm (Putha et al., ), and Particle Swarm Optimization (Shabbir and Omenzetter, ). In this article, an improved quantum‐inspired genetic algorithm (QIGA) is developed because of its excellent performance.…”

Section: Introductionmentioning

confidence: 99%

A Quantum‐Inspired Genetic Algorithm‐Based Optimization Method for Mobile Impact Test Data Integration

Zhao

Guo

Zhou

et al. 2018

Computer aided Civil Eng

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The traditional impact test method needs a large number of sensors deployed on the entire structure, which cannot meet the requirements of rapid bridge testing. A new mobile impact test method is proposed by sequentially testing the substructures then integrating the test data of all substructures for flexibility identification of the entire structure. The novelty of the proposed method is that the quantum-inspired genetic algorithm (QIGA) is proposed to improve computational efficiency by transforming the scaling factor sign determination problem to an optimization problem. Experimental example of a steel-concrete composite slab and numerical example of a three-span continuous rigid-frame bridge are studied which successfully verify the effectiveness of the proposed method. C 2018 Computer-Aided Civil and Infrastructure Engineering.

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Genetic algorithms for the scheduling problem with arbitrary precedence relations and skilled operators

Cited by 28 publications

References 33 publications

Control methodologies for vibration control of smart civil and mechanical structures

Control methodologies for vibration control of smart civil and mechanical structures

Invited Review: Recent developments in vibration control of building and bridge structures

A Quantum‐Inspired Genetic Algorithm‐Based Optimization Method for Mobile Impact Test Data Integration

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