An Improved Differential Evolution Algorithm for Solving Absolute Value Equations

Ning, Guiying; Zhou, Yongquan

doi:10.1007/978-3-319-32557-6_4

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…Constrained optimization problems (Cops) are a type of nonlinear programming problems that often occur in the fields of daily life and engineering applications. ere are usually two ways to solve this problem: deterministic algorithm and random algorithm [16]. Deterministic algorithms generally have high initial requirements, and they are generally unable to solve some problems that are not derivable, the feasible region is not connected, or there is no obvious mathematical expression.…”

Section: Related Research Workmentioning

confidence: 99%

“…Deterministic algorithms generally have high initial requirements, and they are generally unable to solve some problems that are not derivable, the feasible region is not connected, or there is no obvious mathematical expression. Even if some problems can be solved, the solutions obtained are mostly local optimal solutions [16].…”

Section: Related Research Workmentioning

confidence: 99%

“…Secondly, in the evolution update formula of the standard WOA, the evolution information of the current generation is less used. According to previous research on differential evolution algorithm [30], in the evolution process, making full use of current generation information can ensure the global search ability and local search ability of the algorithm; inspired by this, this paper also introduces the current generation information in the update formula of the WOA, set as follows:…”

Section: Improvement Of Mutationmentioning

confidence: 99%

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Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Ning

Cao

2021

Discrete Dynamics in Nature and Society

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In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed. First, the standard WOA is improved from the three aspects of initial population, convergence factor, and mutation operation. At the same time, Gaussian mutation is introduced. Then the nonfixed penalty function method is used to transform the constrained problem into an unconstrained problem. Finally, 13 benchmark problems were used to test the feasibility and effectiveness of the proposed method. Numerical results show that the proposed IWOA has obvious advantages such as stronger global search ability, better stability, faster convergence speed, and higher convergence accuracy; it can be used to effectively solve complex constrained optimization problems.

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Section: Related Research Workmentioning

confidence: 99%

Section: Related Research Workmentioning

confidence: 99%

Section: Improvement Of Mutationmentioning

confidence: 99%

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Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Ning

Cao

2021

Discrete Dynamics in Nature and Society

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“…As a new swarm intelligence optimization algorithm, the whale optimized algorithm still has some shortcomings and still has potential for development. It is of great significance to improve the algorithm and expand its application areas [ 10 , 11 ]. In [ 12 ], a chaotic feedback adaptive whale optimization algorithm (CFAWOA) is proposed to improve the shortcomings of low accuracy in finding the best in complex function optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multistrategy Improved Whale Optimization Algorithm and Its Application

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

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“…Zhang and Wei in [24] exhibited generalized Newton method with combining the semi-smooth and the smoothing Newton steps, and Li [9] used a modified generalized Newton method when all the singular values of A exceed 1. Ning et al [16] considered improved adaptive differential evolution for solving absolute value equations; in this algorithm, they use global and local search. Cruz et al [1] used the inexact non-smooth Newton method, and established global linear convergence of the method.…”

Section: Introduction Recently the Problem Of Finding A Solution Fomentioning

confidence: 99%

The proximal methods for solving absolute value equation

Shahsavari¹,

Ketabchi²

2021

NACO

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In this paper, by considering that the objective function of the least squares NP-hard absolute value equations (AVE) Ax − |x| = b, is nonconvex and non-smooth, two types of proximal algorithms are proposed to solve it. One of them is the proximal difference-of-convex algorithm with extrapolation and another is the proximal subgradient method. The convergence results of the proposed methods are proved under certain assumptions. Moreover, a numerical comparison is presented to demonstrate the effectiveness of the suggested methods.

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An Improved Differential Evolution Algorithm for Solving Absolute Value Equations

Cited by 6 publications

References 9 publications

Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Multistrategy Improved Whale Optimization Algorithm and Its Application

The proximal methods for solving absolute value equation

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