scholarly journals An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ren Qing-dao-er-ji ◽  
Rui Pang ◽  
Yue Chang
Keyword(s):  
Simulated Annealing ◽  
Association Rules ◽  
Inner Mongolia ◽  
Simulated Annealing Algorithm ◽  
Solution Space ◽  
Real Data ◽  
Wine Quality ◽  
Minimum Support ◽  
Support Threshold ◽  
Annealing Algorithm

HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules.

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Location Routing ◽  
Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

An Extended Simulated Annealing Based on the Memory Structure to Solve Redundancy Allocation Problem

2019 ◽  
Vol 18 (04) ◽  
pp. 527-548
Author(s):  
Arash Zaretalab ◽  
Vahid Hajipour
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Solution Space ◽  
Global Optimum ◽  
Allocation Problem ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Annealing Algorithm

One of the most practical optimization problems in the reliability field is the redundancy allocation problem (RAP). This problem optimizes the reliability of a system by adding redundant components to subsystems under some constraints. In recent years, various meta-heuristic algorithms applied to find a local or global optimum solution for RAP in which redundancy strategies are chosen. Among these algorithms, simulated annealing algorithm (SA) is a capable one and makes use of a mathematical analogue to the physical annealing process to finding the global optimum. In this paper, we present a new simulated annealing algorithm named knowledge-based simulated annealing (KBSA) to solve RAP for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. In the KBSA algorithm, the SA part searches the solution space to find good solutions and knowledge model saves the knowledge of good solution and feed it back to the algorithm. In this paper, this approach achieves the optimal result for some instances in the literature. In order to evaluate the performance of the proposed algorithm, it is compared with well-known algorithms in the literature for different test problems. Finally, the results illustrate that the proposed algorithm has a good proficiency in obtaining desired results.

scholarly journals Simulated Annealing Algorithm Combined with Chaos for Task Allocation in Real-Time Distributed Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Wenbo Wu ◽  
Jiahong Liang ◽  
Xinyu Yao ◽  
Baohong Liu
Keyword(s):  
Simulated Annealing ◽  
Distributed Systems ◽  
Real Time ◽  
Task Allocation ◽  
Simulated Annealing Algorithm ◽  
Solution Space ◽  
Solution Quality ◽  
Adaptive Schemes ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm

This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality.

scholarly journals Some Remarks on Replicated Simulated Annealing

2021 ◽  
Vol 182 (3) ◽  
Author(s):  
Vicent Gripon ◽  
Matthias Löwe ◽  
Franck Vermet
Keyword(s):  
Neural Networks ◽  
Simulated Annealing ◽  
Statistical Physics ◽  
Simulated Annealing Algorithm ◽  
Real Data ◽  
Data Bases ◽  
Physics Literature ◽  
Annealing Algorithm

AbstractRecently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statistical physics literature. Among other points, they claim their method is able to find robust configurations. In this paper, we analyze this so called “replicated simulated annealing” algorithm. In particular, we give criteria to guarantee its convergence, and study when it successfully samples from configurations. We also perform experiments using synthetic and real data bases.

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