A particle swarm optimization approach in printed circuit board thermal design

Alexandridis, Alex; Paizis, Evangelos; Chondrodima, Eva; Stogiannos, Marios

doi:10.3233/ica-160536

Cited by 39 publications

(11 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational intelligence (CI) is the scientific domain that uses nature-inspired computational methodologies in order to cope with problems for which conventional mathematical reasoning and modelling can be inadequate, due to the complexity, the uncertainty, or the stochastic nature of them [16]. Genetic algorithms and the particle swarm optimization algorithm are considered to be among the CI methods that are most widely used for optimization [17][18][19][20][21][22].…”

Section: Computational Intelligence Methodsmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

Self Cite

View full text Add to dashboard Cite

The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.

show abstract

Section: Computational Intelligence Methodsmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…A 10‐story structure model subjected to several historical pulse‐like near‐fault ground motions is used to demonstrate the performance of the control method. To address the weighting matrices selection problem of LQR control, Vinodh Kumar, Raaja, and Jerome () propose an adaptive PSO (Alexandridis, ; Boulkabeit, Mthembu, De Lima Neto, & Marwala, ) method to obtain the elements of Q and R matrices and apply it for tracking control of a two‐DOF laboratory helicopter.…”

Section: Linear Control Algorithmsmentioning

confidence: 99%

“…Discussing the internal relationship between the weighing matrix Q and Riccati matrix P, Yang, Li, and Liu (1991) present an instantaneous optimal control with velocity and acceleration feedback for nonlinear and hysteretic structures based on the equivalent linearization method. Through integration of the discrete wavelet transform (WT; Dai, 2017;Dai, Wang, & Zhang, 2015), particle swarm optimization (PSO; Alexandridis, 2017;Shabbir & Omenzetter, 2015), and LQR, Amini, Khanmohammadi Hazaveh, and Abdolahi Rad (2013) present a method to search the optimal control force of the active tuned mass damper (ATMD) where discrete WT is used to determine the local energy distribution of the motion over the frequency bands and PSO is employed to find the gain matrices by updating the weighting matrices. A 10-story structure model subjected to several historical pulse-like near-fault ground motions is used to demonstrate the performance of the control method.…”

Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

View full text Add to dashboard Cite

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.

show abstract

“…In recent years, considerable attention has been devoted to meta‐heuristic methodologies based on social dynamics and/or the behaviour of live beings and inspired by nature (N. Siddique & Adeli, ; N. H. Siddique & Adeli, ). Among the most widely used techniques mentioned in this review are genetic algorithms (Mencıa, Sierra, Mencıa, & Varela, ; Paris, Pedrino, & Nicoletti, ; Park, Oh, & Park, ; Pillon, Pedrino, Roda, & Nicoletti, ), particle swarm otimization (Alexandridis, Paizis, Chondrodima, & Aliaj, ; Boulkabeit, Mthembu, De Lima Neto, & Marwala, ; Shabbir & Omenzetter, ), the harmony search algorithm (N. H. Siddique & Adeli, , ant colony optimization (Dorigo & Di Caro, ), artificial bee colony (Karaboga & Basturk, ), artificial immune systems (de Castro & Timmis, ), genetic programming (Koza, ), tabu search (Gómez, Pacheco, & Gonzalo‐Orden, ), and physics‐based algorithms (N. H. Siddique & Adeli, ) such as the gravitational search algorithm (N. H. Siddique & Adeli, ).…”

Section: Introductionmentioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

A particle swarm optimization approach in printed circuit board thermal design

Cited by 39 publications

References 64 publications

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

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

Contact Info

Product

Resources

About