A Multi-Objective Approach Based on Differential Evolution and Deep Learning Algorithms for VANETs

Taha, Mohammad Bany; Talhi, Chamseddine; Ould-Slimane, Hakima; Alrabaee, Saed; Choo, Kim‐Kwang Raymond

doi:10.1109/tvt.2022.3219885

Cited by 14 publications

(7 citation statements)

References 59 publications

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“…In order to ensure the fairness of simulation experiments, this study selected existing algorithms for UAV trajectory planning and DE-ACO algorithm for comparison. Specifically, this study selects the IACO algorithm from work [32]- [33], the DE algorithm from work [34]- [35], and the IGSO algorithm from work [24], [36] for comparison. To verify the robustness of the algorithm designed in this study, all algorithms were run 30 times.…”

Section: Results Displaymentioning

confidence: 99%

“…Reference [34] applies the differential evolution (DE) algorithm to the trajectory planning problem of storage drones. Taha et al also used an improved DE algorithm when solving robot paths [35]. Works [24] and [36] designed an improved group search optimization algorithm (IGSO) for solving unmanned aerial vehicle trajectory planning problems in three-dimensional environments.…”

Section: Literatures Reviewmentioning

confidence: 99%

“…In this study, a constant time step b T was chosen. Therefore, in order to establish a complete mathematical model of unmanned aerial vehicles, according to references [35]- [36], the constraint conditions are defined as follows.…”

Section: Objective Function and Constraint Conditionsmentioning

confidence: 99%

“…In traditional DE algorithms, the search process consists of three single loops, and the fitness function value of each individual in the population is calculated twice. According to work [35], [39], the time complexity ( ) O DE of the DE algorithm is defined as follows:…”

Section: Computational Time Complexity Analysismentioning

confidence: 99%

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Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

2024

KSII TIIS

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Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

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Section: Results Displaymentioning

confidence: 99%

Section: Literatures Reviewmentioning

confidence: 99%

Section: Objective Function and Constraint Conditionsmentioning

confidence: 99%

Section: Computational Time Complexity Analysismentioning

confidence: 99%

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Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

2024

KSII TIIS

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“…DE algorithm also has lower spatial complexity. Because of its excellent characteristics, the DE algorithm is more conducive to the processing of large-scale, high-complexity optimization problems in many fields, including computer science, biology, and industry ( Cappiello et al, ; Bano, Bashir & Younas, 2020 ; Ibrahim et al, 2015 ; Taha et al, 2023 ; Chen et al, 2022 ; Zhang, Yu & Wu, 2021 ). The classical DE algorithm ( Storn & Price, 1997 ) contains four main steps during the optimization process.…”

Section: Preliminaries Of Studymentioning

confidence: 99%

An improved differential evolution algorithm for multi-modal multi-objective optimization

Qu,

Xiao,

Chen

et al. 2024

PeerJ Computer Science

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Multi-modal multi-objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi-modal multi-objective differential evolution (MMODE) algorithm, called MMODE_AP, for the suit of CEC’2020 benchmark functions. First, two adaptive mutation strategies are adopted to balance exploration and exploitation and improve the diversity in the evolution process. Then, the affinity propagation clustering method is adopted to define the crowding degree in decision space (DS) and objective space (OS). Meanwhile, the non-dominated sorting scheme incorporates a particular crowding distance to truncate the population during the environmental selection process, which can obtain well-distributed solutions in both DS and OS. Moreover, the local PF membership of the solution is defined, and a predefined parameter is introduced to maintain of the local PSs and solutions around the global PS. Finally, the proposed algorithm is implemented on the suit of CEC’2020 benchmark functions for comparison with some MMODE algorithms. According to the experimental study results, the proposed MMODE_AP algorithm has about 20 better performance results on benchmark functions compared to its competitors in terms of reciprocal of Pareto sets proximity (rPSP), inverted generational distances (IGD) in the decision (IGDX) and objective (IGDF). The proposed algorithm can efficiently achieve the two goals, i.e., the convergence to the true local and global Pareto fronts along with better distributed Pareto solutions on the Pareto fronts.

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An energy efficient access control for secured intelligent transportation system for 6G networking in VANET

Pulligilla,

Vanmathi

2024

Peer-to-Peer Netw. Appl.

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A Multi-Objective Approach Based on Differential Evolution and Deep Learning Algorithms for VANETs

Cited by 14 publications

References 59 publications

Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

An improved differential evolution algorithm for multi-modal multi-objective optimization

An energy efficient access control for secured intelligent transportation system for 6G networking in VANET

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