A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

Sun, Youxian; Yang, Tong; Liu, Zujun

doi:10.1016/j.asoc.2019.105744

Cited by 97 publications

(40 citation statements)

References 30 publications

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“…Tables 10, 11 Note that if the results of the two algorithms were the same, the parameter p was N/A. However, this result does not mean that these algorithms have exactly the same performance [51].…”

Section: Statistical Analysesmentioning

confidence: 99%

“…The selected benchmark functions are shown in Tables 1, 2 and 3, where Theoretical best was the global optimal value. These benchmark functions were the classical functions used by many researchers when they study optimization algorithms [47]- [51].…”

Section: A Benchmark Functionsmentioning

confidence: 99%

“…A statistical test was performed to show the importance of the results. This section introduces two non-parametric tests: the Friedman test [53] and the Wilcoxon rank sum test [51]. The Friedman test is a nonparametric test that uses ranks to achieve significant differences in multiple population distributions.…”

Section: Statistical Analysesmentioning

confidence: 99%

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An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

Han¹,

Xu²,

Yue³

et al. 2020

IEEE Access

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The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well. However, its simple search mechanism also leads to the algorithm's slow convergence speed and its ease of falling into local optimization when solving complex optimization problems. In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism. By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced. Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization. The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems. The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms. The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance. INDEX TERMS Crow search algorithm, weight coefficient, spiral search mechanism, engineering design optimization.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: A Benchmark Functionsmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

Han¹,

Xu²,

Yue³

et al. 2020

IEEE Access

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show abstract

“…The properties of the symmetric sine cosine operator are opposite to the original. Two sets of piecewise functions as shown in (26) and (27). The following two sets of formulas have the same probability of being chosen and are considered as new symmetric sine and cosine operator.…”

Section: Symmetric Sine and Cosine Operatormentioning

confidence: 99%

A Symmetric Sine Cosine Algorithm With Adaptive Probability Selection

Wang

Xiang

et al. 2020

IEEE Access

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To improve the ability of the sine cosine algorithm (SCA) in the exploitation process, an improved symmetric SCA with adaptive probability selection (SSCA-APS), is proposed. The search process of this algorithm is divided into early and late stages. In the early stage, the operators of the traditional SCA algorithm continue to be used. In the late stage, three improvements were applied. Firstly, the symmetric sine and cosine operators are proposed. The adaptive probability selection strategy is adopted to integrate original sine and cosine operators and symmetric sine and cosine operators for dynamically adjusting the step size of the search range. Furthermore, to prevent the population from falling into local optimization, Gaussian perturbation is used to mutate the globally optimal individuals of the current generation. In addition, the information of two randomly selected individuals and the globally optimal individual is integrated by quadratic interpolation to maintain population diversity and produce a new individual. 23 test functions were used to verify the performance of the proposed algorithm. The simulation results indicate that the performance of the SSCA-APS algorithm has competitiveness when it is compared with classical SCA and some state-of-the-art SCA variants.

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“…In [36], a refraction-learning-based WOA augmented with a modified Logistic-model-based conversion parameter update rule was developed to make a trade-off between diversity and convergence during the search process of WOA when solving high-dimensional problems. 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].…”

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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A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

Cited by 97 publications

References 30 publications

An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

A Symmetric Sine Cosine Algorithm With Adaptive Probability Selection

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

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