PSO-ELPM: PSO with elite learning, enhanced parameter updating, and exponential mutation operator

With the maturity of 5G and Intelligent Transportation Systems (ITS) technologies and the prospect of Beyond 5G (B5G) and 6G technologies, the limited lifetime and computing of mobile devices pose significant challenges to Quality of Service (QoS). In addition, the problem of inefficient use of computing, storage, communication, and other resources still exists in communication systems. In response to the above issues, Multi-tier Computing Networks (MTCNs) migrate computationally intensive tasks to the cloud, fog, or edge with sufficient resources, thereby realizing energy-efficient collaborative computing and multi-dimensional resource sharing. However, in the MTCN environment with complex heterogeneity, and high-intensity dynamics, how to provide sustainable solutions for resource scheduling strategies is a meaningful issue. Inspired by Virtual Network Embedding (VNE) to decouple physical network configuration, we propose a multi-target-aware dynamic resource scheduling algorithm for MTCN to improve resource flexibility, which is the first attempt in this direction. Specifically, we consider differentiated QoS requirements like computing, storage, bandwidth, delay, etc., and establish multi-target-aware embedded constraints. Additionally, we present a Deep Reinforcement Learning (DRL)-based scheduling network that can interact scientifically and efficiently with the MTCN environment. It extracts environmental information as state input to better focus on dynamic characteristics as well as calculates candidate nodes and links using a three-layer network architecture and related constraints. Furthermore, the learning process is optimized through the combination of the reward mechanism and the gradient descent mechanism. Finally, comparison experiments on three widely used evaluation indicators (long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate) verify that the proposed algorithm has made an average improvement of 19.042%, 2.563%, and 3.932% respectively compared with all baselines.

Multi-Target-Aware Dynamic Resource Scheduling for Cloud-Fog-Edge Multi-Tier Computing Network

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors' residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH's number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime. 

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

In this paper, we present a novel approach called F-CAPSO (Fuzzy Chaos Adaptive Particle Swarm Optimization) to enhance energy efficiency and security of data transmission in Mobile Ad hoc Networks (MANETs). MANETs are wireless networks comprising mobile nodes that encounter challenges such as limited battery power and mobility.Our approach incorporates routing and clustering processes in MANETs and leverages the Fuzzy-based CAPSO algorithm for selecting the optimal Cluster Head (CH). Additionally, we integrate Salp Swarm Algorithm (SSA) with Replacement Method and Sine Cosine Algorithm (SCA) search (SSARM-SCA) algorithm to achieve optimal routing and secure data transmission. A trust-based encryption strategy is employed to establish a secure route.We evaluate the proposed approach using performance metrics such as throughput, network lifetime, end-to-end delay, packet delivery ratio, energy consumption, and routing overhead ratio. The simulation results demonstrate that our approach outperforms four state-of-the-art techniques (MARP-HO, ACO-HSA, GAHC, and HFAPSO) in terms of energy consumption and network lifetime, with improvements observed for up to 3500 rounds.Our proposed approach offers a comprehensive solution to address the challenges of energy efficiency and security in MANETs, making it beneficial for applications such as tracking and military environments. By integrating the Fuzzy-based CAPSO algorithm and the SSARM-SCA routing protocol, we optimize CH selection and routing paths, respectively, leading to better network performance and an extended network lifetime. 

F-CAPSO: Fuzzy Chaos Adaptive Particle Swarm Optimization for Energy-Efficient and Secure Data Transmission in MANET

As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> and acceleration coefficients <inline-formula> <tex-math notation="LaTeX">$c_{1},c_{2}$ </tex-math></inline-formula>, and introduces a controlling factor <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09732987.pdf

CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

The appearance of web-based media has implied genuine and anecdotal stories introduced in such a comparative manner that it can now and then be hard to differentiate the two. Similarly, manipulation of real photos, audios or videos with the help of Artificial Intelligence techniques is done such that it is difficult to distinguish between the real and fake thus called Deepfake. It can happen to big celebrities, politicians, and to layman as well for some malicious purpose. Consequently, this procedure can end up being very threat to human culture subsequently expected to identify it appropriately. This paper intends to tackle this issue by proposing a model that uses Residual Neural Network (ResNet50) and Long Short-term Memory (LSTM) to detect video as fake or real. This approach tries to find flaws in the fake data left behind while its creation using neural based techniques like generative adversarial networks (GAN).

Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory

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CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory