scholarly journals Adaptive Bat Algorithm Optimization Strategy for Observation Matrix

2019 ◽  
Vol 9 (15) ◽  
pp. 3008 ◽  
Author(s):  
Zhihua Cui ◽  
Chunmei Zhang ◽  
Yaru Zhao ◽  
Zhentao Shi
Keyword(s):  
Bat Algorithm ◽  
Reconstruction Error ◽  
Optimization Strategy ◽  
Algorithm Optimization ◽  
Dynamic Adjustment ◽  
Memory Space ◽  
Adjustment Strategy ◽  
Reconstructed Signal ◽  
Observation Matrix ◽  
Signal Accuracy

Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness.

scholarly journals A Dynamic Adjusting Novel Global Harmony Search for Continuous Optimization Problems

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 337 ◽  
Author(s):  
Chui-Yu Chiu ◽  
Po-Chou Shih ◽  
Xuechao Li
Keyword(s):  
Optimization Problems ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Genetic Mutation ◽  
Dynamic Adjustment ◽  
Mutation Probability ◽  
Adjustment Strategy ◽  
Improved Harmony Search ◽  
Searching Ability ◽  
Continuous Optimization Problems

A novel global harmony search (NGHS) algorithm, as proposed in 2010, is an improved algorithm that combines the harmony search (HS), particle swarm optimization (PSO), and a genetic algorithm (GA). Moreover, the fixed parameter of mutation probability was used in the NGHS algorithm. However, appropriate parameters can enhance the searching ability of a metaheuristic algorithm, and their importance has been described in many studies. Inspired by the adjustment strategy of the improved harmony search (IHS) algorithm, a dynamic adjusting novel global harmony search (DANGHS) algorithm, which combines NGHS and dynamic adjustment strategies for genetic mutation probability, is introduced in this paper. Moreover, extensive computational experiments and comparisons are carried out for 14 benchmark continuous optimization problems. The results show that the proposed DANGHS algorithm has better performance in comparison with other HS algorithms in most problems. In addition, the proposed algorithm is more efficient than previous methods. Finally, different strategies are suitable for different situations. Among these strategies, the most interesting and exciting strategy is the periodic dynamic adjustment strategy. For a specific problem, the periodic dynamic adjustment strategy could have better performance in comparison with other decreasing or increasing strategies. These results inspire us to further investigate this kind of periodic dynamic adjustment strategy in future experiments.

scholarly journals Complexity Analysis of Pricing in a Multichannel Supply Chain with Spillovers from Online to Offline Sales

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Fengxia Mai ◽  
Jianxiong Zhang ◽  
Rui Yang ◽  
Xiaojie Sun
Keyword(s):  
Supply Chain ◽  
Dynamic Pricing ◽  
Complexity Analysis ◽  
Spillover Effect ◽  
Dynamic Game ◽  
Period Doubling ◽  
Dynamic Adjustment ◽  
Adjustment Strategy ◽  
Adjustment Speed ◽  
The Stability

In recent years, many manufacturers have been selling their products to online consumers through e-tailers by adopting reselling mode and agency selling mode simultaneously. The sales from the online channels inevitably incur spillover effect to the traditional offline channels. This paper develops a dynamic pricing game model on the basis of a long-term gradient adjustment mechanism for a multichannel supply chain that consists of a manufacturer and an e-tailer and focuses on examining the impacts of spillover effect, agency fee, and adjustment speed on the stability and complexity of the dynamic game system. The results show that both a greater spillover effect and a higher agency fee can make the dynamic game system more stable, and a higher adjustment speed can destabilize the dynamic game system through period doubling bifurcation. Furthermore, it is interesting to find that the destabilization of the game system benefits the e-tailer and the supply chain while having little influence on the manufacturer, and thus the dynamic adjustment strategy may improve the supply chain efficiency.

Compressed Sensing Research on Barn Temperature

2012 ◽  
Vol 461 ◽  
pp. 160-163
Author(s):  
Hong Liang Fu ◽  
Hua Wei Tao ◽  
Zheng Luo
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Error ◽  
Information Storage ◽  
Storage Space ◽  
Stored Grain ◽  
Wrong Decision ◽  
Critical Data ◽  
Reconstructed Signal ◽  
Feedback Error

That compressed sensing is used in online monitoring of stored grain information could reduce the mass of information storage space and transmission bandwidth. However, due to the question that compressed sensing reconstructed error may cause decision-end to make wrong decision, a limited feedback error controlling method is proposed, wrong decision-making caused by reconstruction error is solved through feedback a small number of critical data. Numerical experiments on barn temperature shows that this method, on the basis of costing a small amount of compression ratio, can effectively improve the quality of reconstructed signal.

Sign in / Sign up

Export Citation Format

Share Document