Network characteristics for neighborhood field algorithms

Ao, Nian; Zhao, Mingbo; Li, Qian; Qu, Shaocheng; Wu, Zhou

doi:10.1007/s00521-019-04255-0

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…In the CSO algorithm, the swarm size is 1500 and the iteration number is 10000. e neighborhood field is defined to be the nearest superior particle and inferior particle. e details of such a neighborhood field can be referred to [30,31].…”

Section: Simulationsmentioning

confidence: 99%

A Competitive Swarm Optimizer‐Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV‐Battery Hybrid Systems

Wang

Yang

et al. 2019

Complexity

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A technoeconomic optimization problem for a domestic grid-connected PV-battery hybrid energy system is investigated. It incorporates the appliance time scheduling with appliance-specific power dispatch. The optimization is aimed at minimizing energy cost, maximizing renewable energy penetration, and increasing user satisfaction over a finite horizon. Nonlinear objective functions and constraints, as well as discrete and continuous decision variables, are involved. To solve the proposed mixed-integer nonlinear programming problem at a large scale, a competitive swarm optimizer-based numerical solver is designed and employed. The effectiveness of the proposed approach is verified by simulation results.

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Section: Simulationsmentioning

confidence: 99%

A Competitive Swarm Optimizer‐Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV‐Battery Hybrid Systems

Wang

Yang

et al. 2019

Complexity

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“…Swarm intelligence algorithm is applied to high-dimensional function optimization problems by many scholars. e study in [7] used neighborhood factors to improve the NFO algorithm, which is applied to complex optimization problems and achieves good results. Compared with the traditional particle swarm optimization (PSO) algorithm, the improved particle swarm optimization algorithm in [8] is applied to high-dimensional function optimization because its convergence accuracy in highdimensional function is improved.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Disturbance Wolf Pack Algorithm for Solving Ultrahigh‐Dimensional Complex Functions

Zhu¹,

Wu²,

Li³

et al. 2021

Complexity

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The optimization of high-dimensional functions is an important problem in both science and engineering. Wolf pack algorithm is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new wolf pack algorithm that can accurately compute the optimal value of a high-dimensional function. First, chaotic opposite initialization is designed to improve the quality of initial solution. Second, the disturbance factor is added in the scouting process to enhance the searching ability of wolves, and an adaptive step length is designed to enhance the global searching ability to prevent wolves from falling into the local optimum effectively. A set of standard test functions are selected to test the performance of the proposed algorithm, and the test results are compared with other algorithms. The high-dimensional and ultrahigh-dimensional functions (500 and 1000) are tested. The experimental results show that the proposed algorithm features in good global convergence, high accuracy calculation, strong robustness, and excellent performance in high-dimensional functions.

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Brain storm optimization algorithm for solving knowledge spillover problems

Cheng

Zhang

et al. 2021

Neural Comput & Applic

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Network characteristics for neighborhood field algorithms

Cited by 4 publications

References 42 publications

A Competitive Swarm Optimizer‐Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV‐Battery Hybrid Systems

A Competitive Swarm Optimizer‐Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV‐Battery Hybrid Systems

A Chaotic Disturbance Wolf Pack Algorithm for Solving Ultrahigh‐Dimensional Complex Functions

Brain storm optimization algorithm for solving knowledge spillover problems

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