Path planning for solar-powered UAV in urban environment

Wu, Jianfa; Wang, Honglun; Li, Na; Yao, Ping; Yu, Hui; Yang, Huaidong

doi:10.1016/j.neucom.2017.10.037

Cited by 136 publications

(49 citation statements)

References 27 publications

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“…Various metaheuristics such as Grey wolf optimizer (GWO) [28,29], Whale Optimization Algorithm (WOA) [30], Ant Lion Optimization (ALO) [31], Firefly Algorithm (FA) [32], Particle Swarm Optimization (PSO) [33], and Ant Colony Optimization (ACO) [34] may demonstrate superior efficiencies in tackling feature selection problems when compared to the exact methods [35,36]. Metaheuristic algorithms have shown improved results and efficiencies in dealing with many real-life applications such as path planning [37], clustering [38], and power dispatch [39]. For example, E.S.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Mafarja

Aljarah

Heidari

et al. 2018

Knowledge-Based Systems

365

149

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Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, grasshopper optimization algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and

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Section: Accepted Manuscriptmentioning

confidence: 99%

“…planning of solar-powered UAV [37]. Faris et al also reviewed the recent variants and applications of the GWO [41].The history of metaheuristics is presented in [42].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Mafarja

Aljarah

Heidari

et al. 2018

Knowledge-Based Systems

365

149

View full text Add to dashboard Cite

show abstract

“…In terms of application, Mafarja and Mirjalili 34 proposed a hybrid version of the WOA (WOA‐SA) and applied it for the selection of feature subsets to achieve the best classification accuracy. In Reference 35, an adaptive chaos‐Gaussian switching solving strategy and coordinated decision‐making mechanism were introduced to the basic WOA for the problem of solar drone path planning. Liu et al 36 proposed a hybrid WOA enhanced with Lévy flight and differential evolution (WOA‐LFDE) to solve the job shop scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Ding

Zhao

2020

Concurrency and Computation

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The whale optimization algorithm (WOA) is a new biological meta-heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth-flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed. INDEX TERMS chaotic inertial weightconvergence factorcube mappingmeta-heuristic algorithmwhale optimization algorithm (WOA) 1 INTRODUCTION Meta-heuristic algorithms have the advantages of simplicity and easy implementation. They can effectively avoid falling into a local optima entrapment. They were developed to quickly solve large-scale and complex problems to obtain satisfactory solutions. 1 These algorithms are widely used in production scheduling, 2,3 signal processing, 4 image processing, 5 and dynamic optimization. 6 The idea of meta-heuristic algorithms originated from various biological mechanisms and physical laws. As an important concept of meta-heuristic algorithms, swarm intelligence algorithms obtain optimal solutions by iteratively updating the population. Representative swarm intelligence algorithms include particle swarm optimization (PSO) 7 which simulates the predatory behaviors of bird flocks; genetic algorithm (GA), 8 which simulate the natural evolution of Darwin's biological evolution theory and the genetic mechanism of biological evolution; ant colony optimization (ACO) algorithm, 9 which simulates ant foraging behaviors; artificial bee colony, 10 which simulates the bee collecting behavior; firefly algorithm (FA), 11 which simulates the glowing behavior of fireflies; and the bat algorithm, 12 which simulates the bat hunting behavior via ultrasound. The whale optimization algorithm (WOA) 13 is a new swarm intelligence algorithm proposed by an Australian scholar Mirjalili, inspired by the social behavior of humpback whales. The WOA determines the global optimum by encircling, searching, and attacking the preys. It has a simple principle, few parameter settings, and strong optimization ability; moreover, it successfully solved 29 mathematical and six structural optimization problems. 13 This algorithm has been successfully applied to tasks such as image segmentation, 14 optimized location, 15 and neural network training. 16 However, like other meta-heuristic optimization algorithms, the traditional WOA...

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“…> 1 contrary to exploitation phase, exploration phase permits WOA to request a global search using Equations 14 and 15. 55,56…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Optimal network restructure via improved whale optimization approach

Elazim

Ali

2020

Int J Communication

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SummaryLatterly, reduction of power loss in distribution system is the objective of many researches due to its impact on total costs and voltage profiles. It can be handled by optimal restructure of radial distribution system (RDS). This article introduces an innovative approach to restructure of RDS by electing the optimal switches combination subject to the system operating constraints, which is improved whale optimization approach (IWOA). The suggested approach combines exploitation of WOA with exploration of differential evolution (DE), and thus, it supplies a promising candidate solution. The suggested approach is tested on IEEE 33 and 69 bus RDS. The superiority of the suggested approach compared with other well‐known approaches is verified through simulation results by observation of total losses, cost, and saving. Also, the impact of alterable loading is investigated to prove the effectiveness of the suggested IWOA.

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Path planning for solar-powered UAV in urban environment

Cited by 136 publications

References 27 publications

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Optimal network restructure via improved whale optimization approach

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