Study on the Application of Genetic Algorithms in the Optimization of Wireless Network

Wang, Yan; Xin-xin, Shan; Yan-ming, Sun

doi:10.1016/j.proeng.2011.08.1094

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Genetic algorithms have a slow convergence speed, and fixed crossover and mutation probabilities can disrupt individuals with higher fitness. During the iterative process, there may be frequent occurrences of stagnation or degradation [10]. The specific operational steps are as shown in Table 1:…”

Section: Genetic Algorithmmentioning

confidence: 99%

Research on the Profit Maximization Model for Automatic Pricing and Restocking of Vegetable Commodities

Zhang,

Wang,

Zhou

2024

EHSS

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Due to the perishable nature of fresh agricultural products and the uncertainty of the types and prices of individual items before daily procurement in supermarkets, these establishments typically need to determine daily replenishment and pricing strategies based on the historical sales and demand for various goods. This paper starts from the perspective of maximizing supermarket revenue, predicting the sales of various product categories through seasonal indices. Under the constraints of the sales space for goods, the paper further uses ARIMA time series forecasting for the sales volume and procurement prices of individual items during the corresponding time period. Cost markup equivalent coefficients, daily replenishment quantities, and discount coefficients are considered as decision variables. Based on the demand curve model in economics, the paper takes into account the counteraction of cost markup pricing coefficients on sales volume and the counteraction of discount coefficients on the discounted portion of sales. The objective functions include maximizing supermarket revenue and satisfying the market demand for various categories of vegetable products. The paper adopts a novel sales model of offering discounts on predicted perishable vegetables and establishes a multi-objective programming model, providing the optimal strategies for replenishment quantities and pricing for each item on July 1st.

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Section: Genetic Algorithmmentioning

confidence: 99%

Research on the Profit Maximization Model for Automatic Pricing and Restocking of Vegetable Commodities

Zhang,

Wang,

Zhou

2024

EHSS

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“…[2,3]. To solve these issues, evolutionary algorithm such as Genetic Algorithm (GA), Bees Algorithm (BA), PSO can be used [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The most important part of every evolutionary algorithm is its fitness function.…”

Section: Introductionmentioning

confidence: 99%

Fitness function improvement of evolutionary algorithms used in sensor network optimisations

et al. 2018

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Recent advances in wireless sensor network (WSN) technology are enabling the deployment of large-scale and collaborative sensor networks. WSNs face several challenges such as security, localisation, and energy consumption. To resolve these issues, evolutionary algorithms can be helpful. The core of every evolutionary algorithm is its fitness function. The drawbacks of fitness functions used in the literature will be investigated and some solutions will be suggested. The simulation results clearly show the improvement due to suggested solutions.

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Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage

Al-Mousawi¹

2019

Wireless Netw

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Study on the Application of Genetic Algorithms in the Optimization of Wireless Network

Cited by 6 publications

References 4 publications

Research on the Profit Maximization Model for Automatic Pricing and Restocking of Vegetable Commodities

Research on the Profit Maximization Model for Automatic Pricing and Restocking of Vegetable Commodities

Fitness function improvement of evolutionary algorithms used in sensor network optimisations

Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage

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