The snow Goose algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that are easy to fall into local optimal and prem...
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The snow Goose algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that are easy to fall into local optimal and premature convergence. In order to further improve the optimization performance of the algorithm, this paper proposes an improved snow Goose algorithm (ISGA) based on three strategies according to the real migration habits of snowgeese: (1) Lead goose rotation mechanism. (2) Honk-guiding mechanism. (3) Outlier boundary strategy. Through the above strategies, the exploration and development ability of the original algorithm is comprehensively enhanced, and the convergence accuracy and convergence speed are improved. In this paper, two standard test sets of IEEE CEC2022 and IEEE CEC2017 are used to verify the excellent performance of the improved algorithm. The practical application ability of ISGA is tested through 8 engineering problems, and ISGA is employed to enhance the effect of the clustering algorithm. The results show that compared with the comparison algorithm, the proposed ISGA has a faster iteration speed and can find better solutions, which shows its great potential in solving practical optimization problems.
This paper proposes a novel nature-inspired meta-heuristic algorithm, named snow geese algorithm. It is inspired by the migratory behavior of snowgeese and emulates the distinctive "Herringbone" and "S...
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This paper proposes a novel nature-inspired meta-heuristic algorithm, named snow geese algorithm. It is inspired by the migratory behavior of snowgeese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the snow geese algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the snow geese algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess snow geese algorithm problem-solving capabilities. The primary objective of the snow geese algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the snow geese algorithm is publicly available at https:// github .com /stones3421 /SGA -project.
Customer churn is a significant challenge businesses face when customers abandon or switch to other service providers. Churn may also be as a result of the dissatisfactory service or as a result of increasing prices w...
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ISBN:
(纸本)9798331540661;9798331540678
Customer churn is a significant challenge businesses face when customers abandon or switch to other service providers. Churn may also be as a result of the dissatisfactory service or as a result of increasing prices within the service industry. The churn rate represents one of the biggest strategic concerns for the providers of services, thus they should design a proactive approach to churn which means that churn should be recognized and, if possible, prevented due to the main reasons for dissatisfaction. It was noted that even 1 percent increase in customer retention rate implies relatively significant increase in net present value in the majority of service providing organizations. The main objective of this effort is to build a churn prediction model that will enable the telecom employees to understand which customers are probable to churn. The Hybrid Mountain Gazelle Optimizer with Fire Hawk Optimization (HMG-FHO+) algorithm is used in the experimental strategy for this study to choose relevant features from the large dataset for the churn dataset. Riemann Residual Neural Network (RieRes-NN) paired with the snow geese algorithm for optimization is used to measure the model's performance. The analysis shows that RieRes-NN has a very high capacity of predicting the outcomes with a precision of 93%. About 88% compared to the standard network. The combined RieRes-NN-SG method results in higher accuracy of churn predictions and allows for creating more effective concrete actions to minimize churn rates and increase clients' satisfaction in an organization. Our methodology described here is a major contribution of predictive analytics plus it offers a robust instrument for preventive handling of customers in service-delivery organizations.
This paper applies the Chameleon Swarm algorithm (CSA) and snow geese algorithm (SGA) for optimizing the placement of electric vehicle charge stations (EVCSs), renewable energy sources (RESs), and shunt capacitors (SC...
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This paper applies the Chameleon Swarm algorithm (CSA) and snow geese algorithm (SGA) for optimizing the placement of electric vehicle charge stations (EVCSs), renewable energy sources (RESs), and shunt capacitors (SCs). The actual power ranges of the EVCSs of the Vinfast company in Vietnam are used to check the stabilization of the IEEE 85-node distribution power grid by considering four penetration levels of EVCSs, namely 25%, 50%, 75%, and 100%. All penetration levels of EVCSs violate the operating load voltage limits, and the grid cannot work for all the penetration levels. Different scenarios are performed to find the minimum RES penetration level and the most possible SC penetration level to satisfy the operating voltage limits. The use of only SCs cannot satisfy the voltage limits even for the 25% EVCS penetration level. The placement of RESs provides the capability to maintain voltage within the allowed range for 25% and 50% EVCS penetration but not for 75% and 100%. Using both RESs and SCs, the operating voltage limits are satisfied by using RESs with 1385 kW (about 30.44% of loads and EVCSs) and SCs with 2640 kVAr for the 75% EVCS penetration level and using RESs with 2010 kW (about 38.58% of loads and EVCSs) and SCs with 2640 kVAr (100% of loads) for the 100% EVCS penetration level. The study indicates that the installation of EVCSs should be calculated for stable operation of the distribution power grid, and the combination of both RESs and SCs can satisfy the maximum penetration level of EVCSs in the distribution power grids.
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