flower pollination algorithm
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Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Xin-She Yang ◽  
Mazin Abed Mohammed ◽  

2022 ◽  
Vol 11 (1) ◽  
pp. e33911125020
Francisco Jonatas Siqueira Coelho ◽  
Eulogio Gutierrez Huampo ◽  
Henrique Figueirôa Lacerda ◽  
Arthur Doria Meneses de Freitas ◽  
Abel Guilhermino da Silva Filho

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).

2021 ◽  
Vol 5 (4) ◽  
pp. 461
M. Iqbal Kamboh ◽  
Nazri Bin Mohd Nawi ◽  
Azizul Azhar Ramli ◽  
Fanni Sukma

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

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