An Improved Simulated Annealing Algorithm With Excessive Length Penalty for Fixed-Outline Floorplanning

Huang, Zhipeng; Lin, Zhifeng; Zhu, Ziran; Chen, Jianli

doi:10.1109/access.2020.2980135

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…In formula (8), T(D) is the set of all nodes in the DAG graph of disease D. en, the contribution of disease d to the semantic value of D is expressed as follows:…”

Section: Similarity Calculation Of Obesitymentioning

confidence: 99%

“…Sun et al [ 7 ] used simulated annealing algorithm to optimize the Kriging agent model, so as to realize the crashworthiness optimization of the automobile front end structure. Huang et al [ 8 ] proposed an improved simulated annealing algorithm to deal with the fixed contour plane planning problem of modern buildings. The results show that the algorithm can achieve 100% success rate in any benchmark.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren

Zhao

2021

Computational Intelligence and Neuroscience

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With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

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“…In formula (8), T(D) is the set of all nodes in the DAG graph of disease D. en, the contribution of disease d to the semantic value of D is expressed as follows:…”

Section: Similarity Calculation Of Obesitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren

Zhao

2021

Computational Intelligence and Neuroscience

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“…This process of generation is iterated until an equilibrium is achived and the reduction in the temperature parameter is visualized. Furthermore, the temperature of the sucessive annealing step 𝑇 𝑁𝑒𝑤 is calculated based on the geometric annealing schedule by multiplying the temperature of the current iteration 𝑇 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 with the factor of cooling 𝛼 (generally lies between 0.8 and 0.99) for the reducing the temperature as depicted in Equation (14).…”

Section: Adoption Of Novel Solution Generation Processmentioning

confidence: 99%

Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

Lenin

Tamilarasan

2022

Wireless Pers Commun

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Spotted Hyena Optimization and Simulated Annealing-based NLOS nodes localization scheme for improving warning message dessimination in VANETsAbstract-An accurate localization technique is considered as the significant entity in Vehicular Ad hoc Networks (VANETs) for facilitating emergency message data transmission in diversified critical safety applications. In VANETs, the system of global positioing is generally used for estimating the position of the vehicles in the network for attaining neighborhood awareness in the event of warning message dissemination. However, the existence of green foliages, buildings, indoor parking lots and urban streen canyons introduces NLOS situation that introduces unwanted errors that crumbles the degree of data dissemination in emergency situations. In this paper, Spotted Hyena and Simulated Annealing Optimization Algorithm (SHSAOA)-based positioning scheme was proposed for precise estimation of NLOS nodes. İt included the advantages of improved simulated annealing (SA) integrated into SHOA for establishing better balance between the process of exploitation and exploration in the search space. This positioning approach generated candidate solutions by deriving the merits of the trajectory-based charateristics of SA throughout the algorithmic development process in order to improve the local optimization process. This proposed SHSAOA utilized the distance infotmation that are associated with the vehicle trajectory, number of vehicles and error in distance information for assessing the precise location of the NLOS nodes in the network. The simulation results of the proposed SHSAOA scheme confirmed minimized localization error with maximized accuracy in transmission, warning message transmission rate, channel utilization degree and neighborhood awareness degree with different vehicular density and NLOS nodes.

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“…Physics-based optimization algorithms have been developed based on the simulation of various laws and phenomena in physics. One of the oldest algorithms in this group is Simulated Annealing (SA), which is inspired by the simulation of the annealing process by melting and cooling operations in metallurgy [17,18]. Simulation of the gravitational force that objects exert on each other at different distances has led to the design of a Gravitational Search Algorithm (GSA) [19].…”

Section: Introductionmentioning

confidence: 99%

Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems

Dehghani

Hubálovský

Trojovský³

2021

IEEE Access

258

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Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strategy includes two phases of prey identification and the tail and chase process. The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. The ability of NGO to solve optimization problems is evaluated on sixty-eight different objective functions. To analyze the quality of the results, the proposed NGO algorithm is compared with eight well-known algorithms, particle swarm optimization, genetic algorithm, teaching-learning based optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine predators algorithm. In addition, for further analysis, the proposed algorithm is also employed to solve four engineering design problems. The results of simulations and experiments show that the proposed NGO algorithm, by creating a proper balance between exploration and exploitation, has an effective performance in solving optimization problems and is much more competitive than similar algorithms.

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An Improved Simulated Annealing Algorithm With Excessive Length Penalty for Fixed-Outline Floorplanning

Cited by 17 publications

References 19 publications

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems

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