Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

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).

A two layer differential evolution algorithm for economic emission dispatch with random wind power

This paper proposes a novel two layer differential evolutionary algorithm with multi-mutation strategy (TLDE) for solving the economic emission dispatch (EED) problem involving random wind power. In recent years, renewable energy such as wind power is more and more participated in the power systems to address the problems of fossil energy shortage and environmental pollution. Hence, the EED problem with the availability of random wind power is investigated in this paper. Due to the uncertain nature of wind speed, the Weibull probability distribution function is used to model the random wind power. In order to improve the search ability, TLDE divides the population into two layers according to the fitness ranking, and individuals in the two layers are treated differently to fully investigate their own potential. The two layers can cooperate with each other to further enhance the search performance by utilizing an information sharing strategy. Also, an adaptive restart scheme is introduced to avoid falling into stagnation. The performance of the proposed TLDE is testified on the 40 units system with 2 modified wind turbines. The experimental results demonstrate that the TLDE method can achieve precise dispatch strategy in EED problem with random wind power.

Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems.

Transferable face image privacy protection based on federated learning and ensemble models

Face image features represent significant user privacy concerns. Face images cannot be privately transferred under existing privacy protection methods, and data across various social networks are unevenly distributed. This paper proposes a method for face image privacy protection based on federated learning and ensemble models. A federated learning model based on distributed data sets was established by means of federated learning. On the client side, a local facial recognition model was obtained by local face data training and used as the input of PcadvGAN to train PcadvGAN for several rounds. On the server side, a parameter aggregator based on a differential evolutionary algorithm was established as the discriminator of PcadvGAN server, and a client facial recognition model was ensembled simultaneously. The discriminator of the PcadvGAN server experienced mutation, crossover, and interaction with the ensemble model to reveal the optimal global weight of the PcadvGAN model. Finally, the global optimal aggregation parameter matrix of PcadvGAN was obtained by calculation. The server and the client shared the global optimal aggregation parameter matrix, enabling each client to generate private face images with high transferability and practicality. Targeted attack and non-targeted attack experiments demonstrated that the proposed method can generate high-quality, transferable, robust, private face images with only minor perturbations more effectively than other existing methods.

Asymmetrical Rotor Skewing Optimization in Switched Reluctance Machines Using Differential Evolutionary Algorithm

Minimizing torque ripple in electrical machines for a given application is not a straightforward task, especially when the application imposes certain constraints. There are many techniques to improve torque ripple, either design-based or control-based. In this paper, a new geometry for switched reluctance machines based on rotor poles skewing is proposed to minimize torque ripple. This paper describes a methodology to design an asymmetrical skew rotor—switched reluctance machine using a multi-objective differential evolutionary algorithm. The main parameters of the optimization process are defined, as is the optimization methodology to obtain an improved design with less torque ripple than a conventional one. Moreover, the analytical formulas used in the optimization method, as well as the optimization technique, are deduced and explained in detail. The mathematical model used to simulate the electrical machine and the power converter are also described. Two-dimensional and three-dimensional finite element analyses were also conducted to assess whether 3D effects (end-effect and axial fringing field) affected the results. Finally, a particular case of a high-voltage direct current-controlled generator in the base of the More Electric Aircraft (MEA) concept or an energy storage system as an electrical machine was analyzed, and the results for the improved configuration were compared with those for the conventional one.

Differential evolution algorithm with population knowledge fusion strategy for image registration

Image registration is a challenging NP-hard problem within the computer vision field. The differential evolutionary algorithm is a simple and efficient method to find the best among all the possible common parts of images. To improve the efficiency and accuracy of the registration, a knowledge-fusion-based differential evolution algorithm is proposed, which combines segmentation, gradient descent method, and hybrid selection strategy to enhance the exploration ability in the early stage and the exploitation ability in the later stage. The proposed algorithms have been implemented and tested with CEC2013 benchmark and real image data. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of solution quality, convergence speed, and solution success rate.