scholarly journals Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Zhou ◽  
Wenxian Jia ◽  
Menghan Liu ◽  
Mengying Xu
Keyword(s):  
Simulated Annealing ◽  
Network Lifetime ◽  
Duty Cycle ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Sensor Nodes ◽  
Particle Swarm Algorithm ◽  
Design Algorithm ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm

Large-scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs’ lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs’ lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).

scholarly journals Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Correlation Algorithm ◽  
Annealing Algorithm

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

scholarly journals Multi-Objective Design of Analog Integrated Circuits Using Simulated Annealing with Crossover Operator and Weight Adjusting

2012 ◽  
Vol 7 (1) ◽  
pp. 7-15
Author(s):  
T. O. Weber ◽  
Wilhelmus A. M. V. Noije
Keyword(s):  
Simulated Annealing ◽  
Integrated Circuits ◽  
Pareto Front ◽  
Analog Circuit ◽  
Simulated Annealing Algorithm ◽  
Global Optimum ◽  
Trade Offs ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm ◽  
Better Than

This paper approaches the problem of analog circuit synthesis through the use of a Simulated Annealing algorithm with capability of performing crossovers with past anchor solutions (solutions better than all the others in one of the specifications) and modifying the weight of the Aggregate Objective Function specifications in order to escape local minimums. Search for the global optimum is followed by search for the Pareto front, which represents the trade-offs involved in the design and it is performed using the proposed algorithm together with Particle Swarm Optimization. In order to check the performance of the algorithm, the synthesis of a Miller Amplifier was accomplished in two different situations. The first was the comparison of 40 syntheses for Adaptive Simulated Annealing (ASA), Simulate Annealing/Quenching (SA/SQ) and the proposed SA/SQ algorithm with crossovers using a 20-minute bounded optimization with the aim of comparing the solutions of each method. Results were compared using Wilcoxon-Mann-Whitney test with a significance of 0.05 and showed that simulated annealing with crossovers have higher change of returning a good solution than the other algorithms used in this test. The second situation was the synthesis not bounded by time aiming to achieve the best circuit in order to test the use of crossovers in SA/SQ. The final amplifier using the proposed algorithm had 15.6 MHz of UGF, 82.6 dBV, 61º phase margin, 26 MV/s slew rate, area of 980 μm² and current supply of 297 μA in a 0.35 μm technology and was performed in 84 minutes.

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