Solving time-varying knapsack problem based on binary harmony search algorithm

2013 ◽  
Author(s):  
Ning Li ◽  
Jianqin Liu
Keyword(s):  
Knapsack Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Time Varying

Solving 0–1 knapsack problem by a novel global harmony search algorithm

2011 ◽  
Vol 11 (2) ◽  
pp. 1556-1564 ◽  
Author(s):  
Dexuan Zou ◽  
Liqun Gao ◽  
Steven Li ◽  
Jianhua Wu
Keyword(s):  
Knapsack Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm

scholarly journals Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 839-850
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Ufuk Yolcu
Keyword(s):  
Time Series ◽  
Search Algorithm ◽  
Harmony Search ◽  
Exponential Smoothing ◽  
Harmony Search Algorithm ◽  
Time Varying ◽  
Smoothing Methods ◽  
Forecasting Methods ◽  
Smoothing Parameters ◽  
Exponential Smoothing Method

Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method.

scholarly journals Solving single and multi-objective 01 Knapsack Problem using Harmony Search Algorithm

2020 ◽  
Vol 64 (01) ◽  
pp. 160-167 ◽  
Author(s):  
Amol C. Adamuthe ◽  
Vaishnavi N. Sale ◽  
Sandeep U. Mane
Keyword(s):  
Knapsack Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Multi Objective

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

2013 ◽  
Vol 32 (9) ◽  
pp. 2412-2417
Author(s):  
Yue-hong LI ◽  
Pin WAN ◽  
Yong-hua WANG ◽  
Jian YANG ◽  
Qin DENG
Keyword(s):  
Spectrum Sensing ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Linear
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