An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

Zhang, Cheng-Xu; Zhou, Kai-Qing; Ye, Shao-Qiang; Zain, Azlan Mohd

doi:10.1109/access.2021.3130640

Cited by 16 publications

(8 citation statements)

References 30 publications

(40 reference statements)

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“…The inertia weight is an important parameter in WOA. A constant inertia weight will reduce the efficiency of the algorithm and is not conducive to the global optimization of the algorithm [ 25 , 26 ]. In [ 27 ], it is stated that larger inertia weights are beneficial for global optimization.…”

Section: Whale Optimization Algorithm Incorporates Multiple Improveme...mentioning

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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Section: Whale Optimization Algorithm Incorporates Multiple Improveme...mentioning

confidence: 99%

Multistrategy Improved Whale Optimization Algorithm and Its Application

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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“…More recent SI-based algorithms have further been developed, including the Firefly Algorithm, which is based on the flashing patterns and behavior of tropic fireflies [7], and the Cuckoo Search Algorithm, which is inspired by the brood parasitism of cuckoo species [8], [45]. Additionally, we have the Bat Algorithm, which is derived from the echolocation behavior of microbats [9], and the Flower Pollination Algorithm, which is based on the flower pollination process of flowering plants [10].…”

Section: B Nature-inspired Algorithmsmentioning

confidence: 99%

Philippine Eagle Optimization Algorithm

2022

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We propose the Philippine Eagle Optimization Algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 13 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.

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“…The nonlinear inertia weights ensure that the algorithm makes it difficult to enter the local optimum, and the DE operator successfully enhances the information interchange between algorithm individuals. Through trials, the performance of CSDE is improved 13 . Due to the results, Li et al's proposed improved cuckoo search method significantly increases the algorithm's convergence accuracy and speed.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks

Yang,

Xiang,

Kang

et al. 2024

Baghdad Sci.J

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The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting regional monitoring into point monitoring utilizing the discretization method in WSN. In the experiments, the ICS-PSO-OBL with the standard CS and three CS variants (MACS, ICS-2, and ICS) are utilized to execute the simulation experiment under different numbers of nodes (20 and 30, respectively). The experimental results reveal that the optimized coverage of ICS-PSO-OBL is 18.36%, 7.894%, 15%, and 9.02% higher than that of standard CS, MACS, ICS-2, and ICS when the number of nodes is 20. Moreover, it is 16.94%, 9.61%, 12.27%, and 7.75% higher when the quantity of nodes is 30, the convergence speed of ICS-PSO-OBL, and the distribution of nodes is superior to others.

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An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

Cited by 16 publications

References 30 publications

Multistrategy Improved Whale Optimization Algorithm and Its Application

Multistrategy Improved Whale Optimization Algorithm and Its Application

Philippine Eagle Optimization Algorithm

An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks

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