A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks

Sahoo, Biswa Mohan; Pandey, Hari Mohan; Amgoth, Tarachand

doi:10.1016/j.swevo.2022.101151

Cited by 27 publications

(6 citation statements)

References 59 publications

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“…Subsequently, pairwise comparisons were made to identify the superior individual, which was then incorporated into the offspring population. This procedure was iterated until the population size reached the desired number [40].…”

Section: Selection Operationmentioning

confidence: 99%

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Gao,

Liu,

Jiang

et al. 2024

Mathematics

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To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

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Section: Selection Operationmentioning

confidence: 99%

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Gao,

Liu,

Jiang

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…To present our findings effectively, we have developed a graphical model that visually represents the algorithm's performance metrics, such as accuracy and efficiency. The algorithm introduces a distinctive approach by formulating fitness parameters that combine various crucial factors to determine cluster heads effectively [26]. The novel and adaptive routing protocol combines energy awareness, trust-based mechanisms, and GAs to improve WSN security and energy efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

2024

IJATEE

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“…In this paper, a binary tournament strategy will be used for the selection operation [49]. This involves randomly selecting two individuals, comparing their fitness, and choosing the individual with the better fitness as the winner.…”

Section: Selectionmentioning

confidence: 99%

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

Gao,

Liu,

Wang

et al. 2024

Sustainability

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In order to achieve the goal of low-carbon, efficient delivery using unmanned vehicles, a multi-objective optimization model considering carbon emissions in the problem of optimizing multi-route delivery for unmanned vehicles is proposed. An improved genetic algorithm (IGA) is designed for solving this problem. This study takes into account constraints such as the maximum service duration for delivery, the number of vehicles, and the approved loading capacity of the vehicles, with the objective of minimizing the startup cost, transportation cost, fuel cost, and environmental cost in terms of the carbon dioxide emissions of unmanned vehicles. A combination encoding method based on the integer of the number of trips, the number of vehicles, and the number of customers is used. The inclusion of a simulated annealing algorithm and an elite selection strategy in the design of the IGA enhances the quality and efficiency of the algorithm. The international dataset Solomon RC 208 is used to verify the effectiveness of the model and the algorithm in small-, medium-, and large-scale cases by comparing them with the genetic algorithm (GA) and simulated annealing algorithm (SA). The research results show that the proposed model is applicable to the problem of optimizing the multi-route delivery of unmanned vehicles while considering carbon emissions. Compared with the GA and SA, the IGA demonstrates faster convergence speed and higher optimization efficiency. Additionally, as the problem’s scale increases, the average total cost deviation rate changes significantly, and better delivery solutions for unmanned vehicles are obtained with the IGA. Furthermore, the selection of delivery routes for unmanned vehicles primarily depends on their startup costs and transportation distance, and the choice of different vehicle types has an impact on delivery duration, total distance, and the average number of trips. The delivery strategy that considers carbon emissions shows a 22.6% difference in its total cost compared to the strategy that does not consider carbon emissions. The model and algorithms proposed in this study provide optimization solutions for achieving low-carbon and efficient delivery using unmanned vehicles, aiming to reduce their environmental impact and costs. They also contribute to the development and application of unmanned vehicle technology in the delivery field.

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A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks

Cited by 27 publications

References 59 publications

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

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