Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks

Bình, Huỳnh Thị Thanh; Hanh, Nguyen Thi; Quan, La Van; Dey, Nilanjan

doi:10.1007/s00521-016-2823-5

Cited by 120 publications

(36 citation statements)

References 7 publications

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“…Meng et al [70] presented a modified flower pollination algorithm (MFPA) while attempting to optimize five mechanical engineering design problems. Binh et al [71] proposed the Chaotic Flower Pollination Algorithm (CFPA) to overcome the large computation time and solution instability of FPA. Łukasik et al [72] studied FPA in solving continuous global optimization problems in intelligent systems.…”

Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…Natural pollinators are simulated by using levy flights. Studies have revealed the ingenuity of the FPA algorithm over other metaheuristics [2,7,24,40]. Motivated by this, this algorithm has been employed in the current study in training the NNs of the HNN model.…”

Section: Metaheuristic Supported Neural Networkmentioning

confidence: 99%

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Chatterjee¹,

Datta²,

Dey³

2018

NNW

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The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

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“…There are multiple types of coverage metrics as the sensors are placed with a specific use in mind [9], [10]. The most widely used metric is the area coverage which is defined as the ratio of the area covered by the sensors to the total target area [11], [12], [13]. Special cases of area coverage include point and barrier coverage, where certain locations in the target environment have a higher importance in the coverage problem.…”

Section: Introductionmentioning

confidence: 99%

Effective Area Coverage of 2D and 3D Environments With Directional and Isotropic Sensors

2020

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The problem of finding the optimal deployment of sensors is becoming increasingly important with the growing expansion of the Internet of Things paradigm and increased usage of sensor networks in different applications. During the installation of sensor networks, sensor placement directly affects the performance of the system. The general problem of determining the position and orientation of the sensors with the goal of optimal coverage of a given environment is NP-hard. In this manuscript, an effective stochastic method for the placement of sensors in arbitrarily given two-dimensional and three-dimensional environments is proposed. The method uses models of generic isotropic and directional sensors with the defined probabilistic coverage. The optimization function combining the environment and sensor models based on the area coverage metric is proposed. Three optimization algorithms are compared with regard to obtained coverage score, execution time, and reliability, and the results are presented and discussed.

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Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks

Cited by 120 publications

References 7 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Effective Area Coverage of 2D and 3D Environments With Directional and Isotropic Sensors

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