Cell Assignment in Hybrid CMOS/Nanodevices Architecture Using a PSO/SA Hybrid Algorithm

Sait, Sadiq M.; Sheikh, Ahmad T.

doi:10.1016/s1665-6423(13)71573-6

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…With SA. Sait et al [134] proposed a hybrid of PSO and SA for solving the cell assignment in Cnnos\nano-wire\MOLecular Hybrid (CMOL). Results showed that the proposed hybrid algorithm achieved better solution in terms of buffer count in reasonable time.…”

Section: With Aco Chen and Chienmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

698

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: With Aco Chen and Chienmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

698

380

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“…GA and SA rank 6th and 7th, respectively. SA is third worst with MSE of 0.0863, due to its requirement of large iterative epochs [53]. The worst two algorithms are BP and MBP, which were developed to classify linear problems.…”

Section: Discussionmentioning

confidence: 99%

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

Zhang¹,

Wang²,

Dong³

et al. 2015

PIER

110

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Abstract-(Background)We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.

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“…For cell assigning solution in cnnosnanowireMOLecular Hybrid (CMOL), The fusion of PSO and SA was proposed by Sait et al [53]. Tests have shown that the hybrid method introduced is a safer alternative for buffer count in acceptable time.…”

Section: Pso-sa Hybrid Algorithmmentioning

confidence: 99%

Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

Kumar¹,

Ganesh²,

Bhoopal³

et al. 2021

ISI

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This paper presents a variety of contemporary optimization techniques inspired by the real life in nature. Optimization reveals substantial developments in computing systems as well as has come to be the most encouraging strategy for several design applications. The study is conducted on single-objective, multi-objective, and hybrid optimization strategies. These optimization schemes will be of excellent help to organizations to identify optimum criteria and to improve process as well as product high quality. For selected optimization strategies, the process of formulating the objective function/stiffness function for a minimal issue exists. Over the last few years, the most combinatoric problems of all traditional optimization approaches were solved by using metaheuristic algorithms to have optimal solutions for real-time applications. This paper discussed some of the important and feasible optimization scheme and the related algorithms and approaches.

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Cell Assignment in Hybrid CMOS/Nanodevices Architecture Using a PSO/SA Hybrid Algorithm

Cited by 8 publications

References 18 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

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