Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

Zhou, Jie; Jia, Wenxian; Liu, Menghan; Xu, Min

doi:10.1155/2021/9915133

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Jie Zhou et al 19 have introduced an efficient way to increase the life span of largescale wireless sensor networks by using a special version of SA called "Elite Adaptive Simulated Annealing Algorithm (EASA)".…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

et al. 2021

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Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

et al. 2021

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Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm

Ye,

Shi,

et al. 2024

Sci Rep

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The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. This paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the adaptive simulated annealing (ASA) algorithm is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the sum of absolute values of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

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Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

Cited by 2 publications

References 17 publications

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm

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