Optimal Sensors/Actuators Placement in Smart Structure Using Island Model Parallel Genetic Algorithm

Abstract

Determination of optimal placements of sensors/actuators in large structures is a difficult job as large number of possible combinations leads to a very high computational time and storage. Therefore, this kind of optimization problem demands a parallel implementation of the optimization schemes. Island model genetic algorithm (GA) being inherently parallel has been used for searching optimal placements of collocated sensors/actuators. Numerical simulations have been done for determination of optimal placements of collocated PZT sensors and actuators in smart fiber reinforced shell structures using island model parallel GA (IMPGA) in conjunction with electro-mechanical finite element analysis with an objective of maximizing the controllability index. It has been observed that the present IMPGA-based formulation (due to its migration scheme) not only makes it possible to determine optimal sensors/actuators locations for large structures but also leads to a better solution at a much reduced and achievable computational time. Results from scalability analysis also show that the efficacy of the present method of using IMPGA for determination of optimal sensors/actuators location based on FEA will be more pronounced when actually used for real life problems requiring large number of sensors and actuators.

Review on the use of piezoelectric materials for active vibration, noise, and flow control

“…The optimal locations of a multitude of structures have been investigated with GA based optimization technique. Starting from homogeneous [59][60][61] and composite plates [62], GA based optimization has been utilized to determine the optimal actuator and sensor locations for structures like sandwich panels with regular honeycomb interlayers [63], smart fiber reinforced polymer shell structures [64], cylindrical shells [65], and doubly curved stiffened shells [66,67]. In many of these studies, there are several constraints that add to the complexity of the problem.…”

“…• To determine the optimal locations of piezoelectric actuator/sensor pairs for the active flutter control of supersonic sandwich panels with regular honeycomb interlayers, a non-dominated sorting GA II (which is a non-dominated sorting-based multi-objective evolutionary algorithm), along with a single objective GA was employed [63]. • An island model parallel GA was used to determine the optimal locations for collocated piezoelectric actuators/ sensors on a smart fiber reinforced polymer shell structure [64].…”

Cost Efficient Sensor Node Placement Approach for Using Minimum Number of Node in Wireless Sensor Networks

“…Demetri ve diğ., en iyi düğüm yerleşimini otomatik olarak ve dağıtımdan önce tanımlamak amacıyla uzaktan algılamayı kullanan bir yaklaşım olan kablosuz algılayıcı ağlar için havadan ışık algılama destekli yerleştirme yaklaşımını (LaPS) sunmuşlardır [28]. Nandy ve diğ., eşzamanlı paralel ada modeline dayanan genetik algoritmayı, ortak algılayıcıların / aktüatörlerin optimum yerleşimlerini aramak için kullanmışlardır [29]. Trothe ve diğ., akıllı binalar için arıza teşhis problemini ele almışlardır [30].…”

“…En az sayıda düğüm kullanımı sağlanamaması Olasılık tabanlı düğüm yerleştirme işlemi [4], [27], [29], [32] Kapsama alanı, Rasgele En az sayıda düğüm kullanımı sağlanamaması Genel düğüm yerleştirme işlemi [5], [9], [12], [15], [20], [22], [23], [24], [25], [26], [30], [33] Kapsama alanı En az sayıda düğüm kullanımı sağlanamaması Hedef düğüm yerleştirme işlemi [6], [14], [28], [31] Tablo 1'de, ilgili çalışmaların kullanılan yöntem ve tekniklere göre karşılaştırmalı olarak genel bir özeti verilmektedir.…”

Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach

“…In this case, the passengers in each group choose the same subway schedule ∀p ∈ P m at the rail station ∀m ∈ M, and the design process of the feeder bus routes servicing passengers in each group, i.e., the three core variables y k i , x k ij , and t k i , do not affect each other. In order to solve the large-scale problem [35,36], this paper designs a distributed parallel genetic algorithm, shown in Figure 3. Based on the main working thread dividing the problem into sub-problems and providing the data for the sub-problems, the single-population GA is run independently by single working threads to calculate y k i , x k ij , and t k i of ∀i ∈ I p m (gBest i ), and the optimal solution to the original problem is found by the main working thread in order to summarize the calculation results of the single-population GA, executed concurrently with multiple working threads.…”