A modified quantum particle swarm optimizer applied to optimization design of electromagnetic devices

Rehman, Obaid Ur; Tu, Shanshan; Khan, Shafiullah; Khan, Hashmat; Yang, Shiyou

doi:10.3233/jae-180015

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…Quantum-behaved PSO [229], adaptive PSO [251], genetic PSO with dynamically varying neighborhood technique [252] Hybrid Cellular PSO [220], single-swarm PSO [221], adaptive PSO [222] Self-adaptive PSO with adaptive filtering technique [253] Adaptive PSO [223], PSO with wavelet mutation [224], improved boolean PSO [225], hybrid enhanced PSO, and DE [116] Binary PSO [262], modified quantum PSO [263] Image processing…”

Section: Design Of Electromagnetic Devicesmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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Section: Design Of Electromagnetic Devicesmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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“…The inspiration for this algorithm was the social and cognitive behaviour of swarms. In any PSO, every particle has a velocity and a position vector [38], as shown in Fig. 4.…”

Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

Optimisation‐based training of evolutionary convolution neural network for visual classification applications

Rehman

Waqas

et al. 2020

IET Computer Vision

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Training of the convolution neural network (CNN) is a problem of global optimisation. This study proposed a hybrid modified particle swarm optimisation (MPSO) and conjugate gradient (CG) algorithm for efficient training of CNN. The training involves MPSO–CG to avoid trapping in local minima. Particularly, improvements in the MPSO by introducing a novel approach for control parameters, improved parameters updating criteria, a novel parameter in the velocity update equation, and fusion of the CG allows handling the issues in training CNN. In this study, the authors validate the proposed MPSO algorithm on three benchmark mathematical test functions and also compared with three different variants of the baseline particle swarm optimisation algorithm. Furthermore, the performance of the proposed MPSO–CG is also compared with other training algorithms focusing on the analysis of computational cost, convergence, and accuracy based on a standard problem specific to classification applications on CIFAR‐10 dataset and face and skin detection dataset.

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“…𝐷𝐼𝑆 𝑖 (10) The ratio of the overall amount of control overhead to the total amount of data and control packets generated by the network is then taken into account. To determine the total amount of power utilized by each IoT terminal in the network, we also used Cooja's Energest module.…”

Section: Experimental Analysismentioning

confidence: 99%

Validity of oxygen uptake cut-off criteria in plateau identification during horizontal treadmill running

Marsh¹

2018

J Sports Med Phys Fitness

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Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

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A modified quantum particle swarm optimizer applied to optimization design of electromagnetic devices

Cited by 17 publications

References 16 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

Optimisation‐based training of evolutionary convolution neural network for visual classification applications

Validity of oxygen uptake cut-off criteria in plateau identification during horizontal treadmill running

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