An ensemble of intelligent water drop algorithms and its application to optimization problems

Alijla, Basem O.; Wong, Li-Pei; Lim, Chee Peng; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi

doi:10.1016/j.ins.2015.07.023

Cited by 17 publications

(6 citation statements)

References 55 publications

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“…In the turbulent motion, the turbulent friction consumption is converted from the Reynolds stress to the pulsating kinetic energy. The magnitude of this part of the energy is expressed by formula (9).…”

Section: ) Richardson Numbermentioning

confidence: 99%

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Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

2021

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A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm's performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.INDEX TERMS meteorology-aware path planning, UAV, dynamic environment, improved intelligent water drops algorithm.

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Section: ) Richardson Numbermentioning

confidence: 99%

“…The intelligent water drops (IWD) algorithm is inspired by the behavior of rivers and the interaction of water drops and soils in the river bed. This algorithm has been applied to many fields of natural science and engineering science, demonstrating great advantages and potential [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

2021

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“…Since then, the algorithm has been applied to solve several optimization problems, such as the n-queen puzzle multidimensional knapsack problem [ 33 ], multilevel thresholding of gray-level images [ 33 ], multi-objective job shop scheduling in scheduling system [ 40 ], optimum reservoir operation in water resources systems [ 41 ], robot path planning in robotics [ 42 ], economic load dispatch problem in power systems [ 43 ], feature selection with rough set [ 44 ], search and selection optimization processes [ 45 ], and examination time-tabling scheduling problem [ 46 ]. (Reference could be made to [ 47 ] for a comprehensive summary of the various problems that have been successfully solved using the IWD algorithm).…”

Section: Metaheuristic Solution To Mdvrpmentioning

confidence: 99%

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

et al. 2018

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The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

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“…We will test the adaptive chaotic PSO [296], water drop algorithm [297], bacterial chemotaxis optimization (BCO) [298], grey wolf optimization [299], social spider optimization [300], etc.…”

Section: Future Researchesmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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An ensemble of intelligent water drop algorithms and its application to optimization problems

Cited by 17 publications

References 55 publications

Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

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