Image based reconstruction using hybrid optimization of simulated annealing and genetic algorithm

In this paper we address a rectangular packing problem (RPP), which is one of the most difficult NP-complete problems. First, greedy biggest space sequencing (GBSS) is presented as a new placement strategy, which is very essential to RPP. Then, borrowing from the respective advantages of the two algorithms, genetic algorithm (GA) and simulated annealing (SA), a hybrid optimization policy is developed. The hybrid GASA is subjected to a test using a set of benchmarks. Compared to other approaches from the literature the hybrid optimization strategy performs better.

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Application of a uniform design simulated annealing genetic algorithm in separation condition optimization of 15 drugs

Artificial Intelligence and Industrial Application ◽

10.2495/aiia140431 ◽

2015 ◽

Author(s):

X. G. Chen ◽

T. Zhang

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Uniform Design ◽

Separation Condition ◽

Simulated Annealing Genetic Algorithm ◽

Condition Optimization

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Hybrid Genetic Algorithm and Simulated Annealing for Clustering Microarray Gene Expression data

Journal of Physics Conference Series ◽

10.1088/1742-6596/1767/1/012034 ◽

2021 ◽

Vol 1767 (1) ◽

pp. 012034

Author(s):

M Pandi ◽

T Sivakumar ◽

N Senthil Madasamy ◽

N Sadhasivam

Keyword(s):

Gene Expression ◽

Genetic Algorithm ◽

Simulated Annealing ◽

Gene Expression Data ◽

Hybrid Genetic Algorithm ◽

Microarray Gene Expression Data ◽

Expression Data ◽

Microarray Gene Expression ◽

Microarray Gene

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Parallel recombinative simulated annealing: A genetic algorithm

Parallel Computing ◽

10.1016/0167-8191(94)00071-h ◽

1995 ◽

Vol 21 (1) ◽

pp. 1-28 ◽

Cited By ~ 179

Author(s):

Samir W. Mahfoud ◽

David E. Goldberg

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing

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An improved simulated annealing genetic algorithm of EEG feature selection in sleep stage

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) ◽

10.1109/apsipa.2016.7820683 ◽

2016 ◽

Cited By ~ 1

Author(s):

Yundong Ji ◽

Xiangeng Bu ◽

Jinwei Sun ◽

Zhiyong Liu

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Simulated Annealing ◽

Sleep Stage ◽

Simulated Annealing Genetic Algorithm

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Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.651.548 ◽

2013 ◽

Vol 651 ◽

pp. 548-552

Author(s):

Parinya Kaweegitbundit

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Parallel Machines ◽

Flow Shop ◽

Memetic Algorithm ◽

Flow Shop Scheduling ◽

Hybrid Flow Shop ◽

Scheduling Problems ◽

Two Stage ◽

Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

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