Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems

Bathina, Venkateswararao; Devarapalli, Ramesh; Márquez, Fausto Pedro García

doi:10.1007/s42835-022-01301-1

Cited by 9 publications

(5 citation statements)

References 50 publications

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“…PPE is mostly implemented using the population growth and population competition models. Equation ( 1) is commonly used to represent the population growth model, and the specific implementation employs a logical difference Equation (2). The Equation (3) describes the population competition model.…”

Section: Phasmatodea Population Evolution Algorithm (Ppe)mentioning

confidence: 99%

“…In Equation ( 1), p is the population number, r is the population's effective growth rate, and K is the population's maximum environmental bearing capacity in space. In Equation (2), taking the value of K in Equation (1) as one, a represents the growth rate, p ranges from 0 to 1, and a ranges from 0 to 4. t represents the current number of iterations, t ranges from 0 to Max_gen, and Max_gen represents the maximum number of iterations. In Equation (3), q is the number of populations closest to the current population selected by the population competition condition, and r 1 is the population's effective growth rate.…”

Section: Phasmatodea Population Evolution Algorithm (Ppe)mentioning

confidence: 99%

“…Meta-heuristic algorithms are extensions of heuristic algorithms, which are broadly classified as swarm intelligence algorithms, evolutionary algorithms, and physical algorithms. Here are some typical examples of swarm intelligence algorithms: cuckoo search algorithm (CSA) [2,3], artificial bee colony algorithm (ABC) [4,5], bat algorithm In order to test the ability of HP_PPE to solve practical problems, we take the AGV workshop material scheduling problem as an application example of the algorithm. AGV is an automated guided vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…Meta-heuristic algorithms are extensions of heuristic algorithms, which are broadly classified as swarm intelligence algorithms, evolutionary algorithms, and physical algorithms. Here are some typical examples of swarm intelligence algorithms: cuckoo search algorithm (CSA) [ 2 , 3 ], artificial bee colony algorithm (ABC) [ 4 , 5 ], bat algorithm (BA) [ 6 ], salp swarm algorithm (SSA) [ 7 ], pigeon optimization algorithm (PIO) [ 8 ], etc. They are global optimization algorithms with continuous variables and multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling

Han

Chen

Yan

et al. 2023

Entropy

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The phasmatodea population evolution algorithm (PPE) is a recently proposed meta-heuristic algorithm based on the evolutionary characteristics of the stick insect population. The algorithm simulates the features of convergent evolution, population competition, and population growth in the evolution process of the stick insect population in nature and realizes the above process through the population competition and growth model. Since the algorithm has a slow convergence speed and falls easily into local optimality, in this paper, it is mixed with the equilibrium optimization algorithm to make it easier to avoid the local optimum. Based on the hybrid algorithm, the population is grouped and processed in parallel to accelerate the algorithm’s convergence speed and achieve better convergence accuracy. On this basis, we propose the hybrid parallel balanced phasmatodea population evolution algorithm (HP_PPE), and this algorithm is compared and tested on the CEC2017, a novel benchmark function suite. The results show that the performance of HP_PPE is better than that of similar algorithms. Finally, this paper applies HP_PPE to solve the AGV workshop material scheduling problem. Experimental results show that HP_PPE can achieve better scheduling results than other algorithms.

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Section: Phasmatodea Population Evolution Algorithm (Ppe)mentioning

confidence: 99%

Section: Phasmatodea Population Evolution Algorithm (Ppe)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Meta-heuristic algorithms are extensions of heuristic algorithms, which are broadly classified as swarm intelligence algorithms, evolutionary algorithms, and physical algorithms. Here are some typical examples of swarm intelligence algorithms: cuckoo search algorithm (CSA) [ 2 , 3 ], artificial bee colony algorithm (ABC) [ 4 , 5 ], bat algorithm (BA) [ 6 ], salp swarm algorithm (SSA) [ 7 ], pigeon optimization algorithm (PIO) [ 8 ], etc. They are global optimization algorithms with continuous variables and multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling

Han

Chen

Yan

et al. 2023

Entropy

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“…Nadimi-Shahraki et al [24] introduced an adaptive movement step design based on a new multi-trial vector approach, which combines different search strategies in the form of trial vector producers. On this basis, the improved new swarm intelligence algorithm is applied to feature selection [25][26][27], data prediction [28][29][30], engineering design [31,32], optimal power flow optimization [33][34][35] and other scenarios.…”

Section: Related Workmentioning

confidence: 99%

Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine

Ye,

Yang,

Sun

2023

Sustainability

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The traditional e-commerce business chain is being reconstructed around the content of short videos and live streams, and the interest e-commerce is thriving as a new trend in the e-commerce industry. Diversified content promotes the rapid development of interest e-commerce. For consumers, their preferences for different content reflect their consumption level to a certain extent. The purpose of this study is to accurately predict the purchasing power level with the consumer content preference, and provide new ideas for interest e-commerce business. In this paper, the new swarm intelligence algorithm is used to find the optimal misclassification cost, and three cost-sensitive models are established. On this basis, the content preference of interest e-commerce consumers is used to predict the level of purchasing power. The results show that the content preference of interest e-commerce consumers, such as “fashion”, “photography” and “interpretation”, have a significant effect on the prediction of purchasing power at the 95% confidence level. The accuracies of the optimized cost-sensitive support vector machine in predicting consumer purchasing power are all above 0.9, and the highest is 0.9792. This study effectively alleviates the problem that the classification results tend to be biased towards negative samples, especially when the imbalanced rate of the sample is high. It not only provides researchers with an efficient parameter optimization method, but also reflects the relationship between consumer content preference and purchasing power, providing data support for interest e-commerce operations.

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study

Got,

Zouache,

Moussaoui

et al. 2023

J Bionic Eng

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Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems

Cited by 9 publications

References 50 publications

A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling

A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling

Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study

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