Comparison of Parameter-Setting-Free and Self-adaptive Harmony Search

2018 ◽  
pp. 105-112 ◽  
Author(s):  
Young Hwan Choi ◽  
Sajjad Eghdami ◽  
Thi Thuy Ngo ◽  
Sachchida Nand Chaurasia ◽  
Joong Hoon Kim
Keyword(s):  
Harmony Search ◽  
Parameter Setting ◽  
Self Adaptive

A Self-Adaptive Harmony Search Algorithm for Unconstrained Optimization Problems

2014 ◽  
Vol 596 ◽  
pp. 192-195
Author(s):  
Ping Zhang ◽  
Peng Sun ◽  
Yi Ning Zhang ◽  
Guo Jun Li
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Parameter Setting ◽  
Unconstrained Optimization Problems ◽  
Suitable Parameter ◽  
Parameter Values ◽  
Self Adaptive

Recently, a new meta-heuristic optimization algorithm–harmony search (HS) was developed, which imitates the behaviors of music improvisation. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to select suitable parameter values. In this paper, a self-adaptive harmony search algorithm (SaHS) proposed. In this algorithm, we design a new parameter setting strategy to directly tune the parameters in the search process, and balance the process of exploitation and exploration. Finally, we use SaHS to solve unconstrained optimization problems so as to profoundly study and analyze the performance of the SaHS. The results show that the SaHS has better convergence accuracy than the other three harmony search algorithms.

scholarly journals A Novel Self-Adaptive Harmony Search Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Kaiping Luo
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Evolutionary Strategies ◽  
Continuous Variables ◽  
Parameter Setting ◽  
Constrained Problems ◽  
New Variant ◽  
Self Adaptive

The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS), improved harmony search (IHS), and other self-adaptive variants for large-scale optimization problems and constrained problems.

Semi-self-adaptive harmony search algorithm

2017 ◽  
Vol 16 (4) ◽  
pp. 619-636 ◽  
Author(s):  
Xinchao Zhao ◽  
Zhaohua Liu ◽  
Junling Hao ◽  
Rui Li ◽  
Xingquan Zuo
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Self Adaptive

scholarly journals Advanced Parameter-Setting-Free Harmony Search Algorithm

2020 ◽  
Vol 10 (7) ◽  
pp. 2586
Author(s):  
Yong-Woon Jeong ◽  
Seung-Min Park ◽  
Zong Woo Geem ◽  
Kwee-Bo Sim
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Parameter Setting ◽  
Additional Memory ◽  
Memory Utilization ◽  
Number Of Iterations

In this paper, we propose an advanced parameter-setting-free (PSF) scheme to solve the problem of setting the parameters for the harmony search (HS) algorithm. The use of the advanced PSF method solves the problems of the conventional PSF scheme that results from a large number of iterations and shows good results compared to fixing the parameters required for the HS algorithm. In addition, unlike the conventional PSF method, the advanced PSF method does not use additional memory. We expect the advanced PSF method to be applicable to various fields that use the HS algorithm because it reduces the memory utilization for operations while obtaining better results than conventional PSF schemes.

scholarly journals Self-Adaptive Global-Best Harmony Search Algorithm-Based Airflow Control of a Wells-Turbine-Based Oscillating-Water Column

2020 ◽  
Vol 10 (13) ◽  
pp. 4628 ◽  
Author(s):  
Fares M’zoughi ◽  
Izaskun Garrido ◽  
Aitor J. Garrido ◽  
Manuel De La Sen
Keyword(s):  
Water Column ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
The Self ◽  
Oscillating Water Column ◽  
Wells Turbine ◽  
Control Scheme ◽  
Tuning Methods ◽  
Self Adaptive

The Harmony Search algorithm has attracted a lot of interest in the past years because of its simplicity and efficiency. This led many scientists to develop various variants for many applications. In this paper, four variants of the Harmony search algorithm were implemented and tested to optimize the control design of the Proportional-Integral-derivative (PID) controller in a proposed airflow control scheme. The airflow control strategy has been proposed to deal with the undesired stalling phenomenon of the Wells turbine in an Oscillating Water Column (OWC). To showcase the effectiveness of the Self-Adaptive Global Harmony Search (SGHS) algorithm over traditional tuning methods, a comparative study has been carried out between the optimized PID, the traditionally tuned PID and the uncontrolled OWC system. The results of optimization showed that the Self-Adaptive Global Harmony Search (SGHS) algorithm adapted the best to the problem of the airflow control within the wave energy converter. Moreover, the OWC performance is superior when using the SGHS-tuned PID.

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