Parallel simulated annealing with adaptive temperature determined by genetic algorithm

Miki, Mitsunori; Hiroyasu, Tomoyuki; Yoshida, Takeshi; Fushimi, Toshihiko

doi:10.1109/icsmc.2002.1176046

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…With an appropriate annealing schedule, an SA run is statistically guaranteed to reach the global optimum [35,36]. The TPSA, instead of a temporal cooling schedule, probabilistically exchanges information collected from multi-process annealing at fixed but different temperatures, thereby efficiently improving the overall rate of convergence to the solution [37,38].…”

Section: Implementation Of Local Manifold Distance-based Regressionmentioning

confidence: 99%

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series

Kawatsu

2024

R. Soc. Open Sci.

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Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator–prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator–prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

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Section: Implementation Of Local Manifold Distance-based Regressionmentioning

confidence: 99%

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series

Kawatsu

2024

R. Soc. Open Sci.

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“…One of the techniques that try to find a suitable temperature schedule was proposed in [2]. They parallelize simulated annealing while using GA to determine the temperature schedule adaptively (PSA\ATGA).…”

Section: Simulated Annealingmentioning

confidence: 99%

“…It was reported in the work presented by Miki et al [2] that selecting a good temperature schedule highly affects the performance of SA, both time-wise and accuracy-wise. Consequently, finding a method that tunes SA parameters is a very important issue addressed by several studies [2], [3].…”

Section: Introductionmentioning

confidence: 96%

An integral approach for Geno-Simulated Annealing

Hassan

Karray

Kamel

et al. 2010

2010 10th International Conference on Hybrid Intelligent Systems

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Global optimization is the problem of finding the global optimum of any given function in a certain search space. Simulated Annealing (SA) and Genetic Algorithms (GA) are among the well-known techniques used for global optimization. Adjusting the parameters of SA such as the temperature schedule and the neighborhood range plays an important role in the performance of the algorithm. Furthermore, many studies in literature showed that the best values for SA parameters depend on the optimization problem. We introduce a novel hybrid approach that uses SA to solve an optimization problem and uses GA simultaneously to adapt the parameters of SA. This new approach is referred to as Geno-Simulated Annealing (GSA). It does not require any predefined values for the parameters of SA. To evaluate the performance of the proposed approach, we used seven well-known benchmark optimization functions. The obtained results indicate the superiority of the proposed approach as compared to a similar approach and to conventional SA.

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“…However, it has a disadvantage in local minima. Thus, simulated annealing [ 18 ] which has a strong ability for finding the local optimal result is introduced to avoid the problem of local minima. SA mainly consists of the repeating of two steps: a generation mechanism and an acceptance criterion.…”

Section: Distributed Optimization Algorithm For Energy-efficient Coveragementioning

confidence: 99%

Distributed Particle Swarm Optimization and Simulated Annealing for Energy-efficient Coverage in Wireless Sensor Networks

Wang

et al. 2007

Sensors

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Abstract:The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and Dijkstra's algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications.

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Parallel simulated annealing with adaptive temperature determined by genetic algorithm

Cited by 3 publications

References 10 publications

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series

An integral approach for Geno-Simulated Annealing

Distributed Particle Swarm Optimization and Simulated Annealing for Energy-efficient Coverage in Wireless Sensor Networks

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