نتایج جستجو برای: simulated annealing algorithm sa

تعداد نتایج: 900894  

2011
Weng Cho Chew B. Mhamdi K. Grayaa

Recently, the use of the particle swarm optimization (PSO) technique for the reconstruction of microwave images has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, the basic PSO algorithm is easily trapping into local minimum and may lead to the premature convergence. When a local optimal s...

2017
Way Kuo Rui Wan

GA genetic algorithm HGA hybrid genetic algorithm SA simulated annealing algorithm ACO ant colony optimization TS tabu search IA immune algorithm GDA great deluge algorithm CEA cellular evolutionary approach NN neural network NFT near feasible threshold UGF universal generating function MSS multi-state system RB/i/j recovery block architecture that can tolerate i hardware and j software faults ...

Journal: :IJISTA 2016
Changying Wang Min Lin Yiwen Zhong Hui Zhang

Simulated annealing (SA) algorithm is a popular intelligent optimisation algorithm, but its efficiency is unsatisfactory. To improve its efficiency, this paper presents a swarm SA (SSA) algorithm by exploiting the learned knowledge from searching history. In SSA, a swarm of individuals run SA algorithm collaboratively. Inspired by ant colony optimisation (ACO) algorithm, SSA stores knowledge in...

2012
Tania Mara Ferla Guilherme Flach Ricardo Reis

This paper presents an educational tool which can be used for teaching the Simulated Annealing (SA) algorithm. The SA is applied to solve integrated circuit placement. SA is an effective method to solve NPcomplete problems as circuit placement. The interface developed in this work provides a visualization of the execution steps of the SA, which makes the tool more iterative and didactic. The to...

Journal: :CoRR 2014
Gamal Abd El-Nasser A. Said Abeer M. Mahmoud El-Sayed M. El-Horbaty

Quadratic Assignment Problem (QAP) is an NPhard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP) and analyze their performance in terms of both runtime effici...

Gholam-Abass Barani Kourosh Qaderi Marzieh Mohammadi,

Earth dam is a structure as homogeneous or non-homogeneous forms for raising water level or water supply. Earth dam consist of different parts that one of the main parts is clay core. Choosing an optimal non permeable core which causes reduction of seepage through dam body and also being stable is necessary. The objective of this research is to optimize the geometry of earth dam clay core such ...

2012
Y. Chakrapani K. Soundera Rajan

In this paper a hybrid technique of Genetic Algorithm and Simulated Annealing (HGASA) is applied for Fractal Image Compression (FIC). With the help of this hybrid evolutionary algorithm effort is made to reduce the search complexity of matching between range block and domain block. The concept of Simulated Annealing (SA) is incorporated into Genetic Algorithm (GA) in order to avoid pre-mature c...

2014
Sadan Kulturel-Konak Abdullah Konak

In this paper, an unequal area Cyclic Facility Layout Problem (CFLP) is studied. Dynamic and seasonal nature of the product demands results in the necessity for considering the CFLP where product demands as well as the departmental area requirements are changing from one period to the next one. Since the CFLP is NP-hard, we propose a Simulated Annealing (SA) metaheuristic with a dynamic tempera...

2014
Nitin Kumar Jhankal Dipak Adhyaru

Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. Simula...

Journal: :Computational Statistics & Data Analysis 2007
Zheng Yang Zheng Tian Zixia Yuan

The log-likelihood function of threshold vector error correction models is neither differentiable, nor smooth with respect to some parameters. Therefore, it is very difficult to implement maximum likelihood estimation (MLE) of the model. A new estimation method, which is based on a hybrid algorithm and MLE, is proposed to resolve this problem. The hybrid algorithm, referred to as genetic-simula...

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