An improved whale optimization algorithm for forecasting water resources demand

Guo, Wenyan; Liu, Ting; Dai, Fang; Xu, Peng

doi:10.1016/j.asoc.2019.105925

Cited by 84 publications

(29 citation statements)

References 45 publications

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“…WOA recently emerged as a beneficial algorithm, which is applied in many practical problems such as forecasting water resources demand [40], flow shop scheduling problem [41], opinion leader detection in online social network [42], medical diagnosis [43]. However, it was first proposed in [44], which mimicked the unusual social behaviors and the engaging hunting activities of humpback whales.…”

Section: Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

et al. 2020

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The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence. The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.

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Section: Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

et al. 2020

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“…where X * is the best solution obtained so far. If |A| ≥ 1 the humpback whales balance between searching prey using equations ( 15) and ( 16); otherwise, the encircling prey using equations ( 19) and (20). WOA has different parameters to control the behavior of the population, and the main advantage of it is its capability in reproducing the mechanism lying behind the chase for prey.…”

Section: Woamentioning

confidence: 99%

“…A chaotic whale optimization algorithm (CWOA) is proposed, which combines the features of standard WOA with chaotic maps to improve its performance to find the set of parameters that model solar cells [19]. An improved WOA based on social learning and wavelet mutation strategy designs a new linear incremental probability, which increases the possibility of global search [20].…”

Section: Iwoa It Can Be Observed That the Main Advantage Ofmentioning

confidence: 99%

An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

Wang

Tian

Lin

et al. 2020

Discrete Dynamics in Nature and Society

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Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically.

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“…In this context, in order to solve the premature convergence of WOA and modify the exploration process, an enhanced WOA based on quadratic interpolation was proposed [37]. In addition, modified versions of WOA have achieved remarkable results in other applications, such as water resources demand estimation [38], maximizing the power capture of variable-speed wind turbines [39], task allocation [40], parameter identification of solar cell diode model [41], quadratic assignment problem [42], terminal voltage control of fuel cells [43], and shortterm natural gas consumption prediction [44]. Although these modifications have yielded some performance enhancements to the conventional WOA, they still suffer from other drawbacks such as lack of exploitation accuracy [36]- [39], [40], lack of exploration accuracy and getting stuck in local optima [34], [39], [41], [42], [44], and low convergence rate [34], [35], [38]- [41], that need to be adequately addressed.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

2020

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Parameters identification of isolated wind-diesel power systems (WDPS) is a significant issue in stability analysis of the power system as well as guaranteeing the power generation through the control system. In this paper, enhanced whale optimization algorithms (EWOA) are proposed to deal with the parameter identification problem of a WDPS system. The proposed EWOA effectively tackles the premature convergence problem of WOA by splitting the population into two subpopulations and updating the position of each whale according to the position of the best agent in its current subpopulation, the position of the other subpopulation's best agent, and the position of the best neighboring agent. Furthermore, fractional chaotic maps are embedded in the search process of EWOA to increase its performance in terms of accuracy. For validation purposes, the proposed algorithms are applied to identify the unknown parameters of WDPS, where different statistical analyzes and comparisons are carried out with other recent state-of-the-art algorithms. Simulation results confirm that the algorithms have less deviation in parameter estimation, more convergence speed, and higher precision in comparison with other algorithms. INDEX TERMS Optimization, parameter identification, whale optimization algorithm, wind-diesel power system.

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An improved whale optimization algorithm for forecasting water resources demand

Cited by 84 publications

References 45 publications

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

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