Portfolio optimization using genetic algorithm and harmony search algorithm with varying operators and parameter values

2019 ◽  
Author(s):  
Kee Huong Lai ◽  
Woon Jeng Siow ◽  
Ahmad Aniq bin Mohd Nooramin Kaw ◽  
Pauline Ong ◽  
Zarita Zainuddin
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Parameter Values

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.

QUANTUMIZED GENETIC ALGORITHM FOR SEGMENTATION AND OPTIMIZATION TASKS

2020 ◽  
Vol 32 (03) ◽  
pp. 2050022
Author(s):  
Malihe Sabeti ◽  
Laleh Karimi ◽  
Naemeh Honarvar ◽  
Mahsa Taghavi ◽  
Reza Boostani
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Skeletal Maturity ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
The Other ◽  
Left Hand ◽  
Optimization Task ◽  
Complex Functions ◽  
Representation Scheme

Specialists mostly assess the skeletal maturity of short-height children by observing their left hand X-Ray image (radiograph), whereas precise separation of areas capturing the bones and growing plates is always not possible by visual inspection. Although a few attempts are made to estimate a suitable threshold for segmenting digitized radiograph images, their results are not still promising. To finely estimate segmentation thresholds, this paper presents the quantumized genetic algorithm (QGA) that is the integration of quantum representation scheme in the basic genetic algorithm (GA). This hybridization between quantum inspired computing and GA has led to an efficient hybrid framework that achieves better balance between the exploration and the exploitation capabilities. To assess the performance of the proposed quantitative bone maturity assessment framework, we have collected an exclusive dataset including 65 left-hand digitized images, aged from 3 to 13 years. Thresholds are estimated by the proposed method and the results are compared to harmony search algorithm (HSA), particle swarm optimization (PSO), quantumized PSO and standard GA. In addition, for more comparison of the proposed method and the other mentioned evolutionary algorithms, ten known benchmarks of complex functions are considered for optimization task. Our results in both segmentation and optimization tasks show that QGA and GA provide the best optimization results in comparison with the other mentioned algorithms. Moreover, the empirical results demonstrate that QGA is able to provide better diversity than that of GA.

Dynamic Adaptive Harmony Search Algorithm for Optimization

2011 ◽  
Vol 474-476 ◽  
pp. 1666-1671
Author(s):  
Yi Wen Wang ◽  
Min Yao
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Heuristic Optimization ◽  
Perfect State ◽  
Suitable Parameter ◽  
Improved Harmony Search ◽  
Parameter Values ◽  
Pitch Adjustment ◽  
Harmony Memory

A new meta-heuristic optimization algorithm–harmony search is conceptualized using the musical improvisation process of searching for a perfect state of harmony. 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, we proposed a novel algorithm to dynamically adapt the harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) and distance bandwidth (BW). The experimental results revealed the superiority of the proposed method to the original HS, improved harmony search (IHS) and global-best harmony search (GHS).

scholarly journals Resource Allocation Schemes Based on Intelligent Optimization Algorithms for D2D Communications Underlaying Cellular Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xujie Li ◽  
Lingjie Zhou ◽  
Xing Chen ◽  
Ailin Qi ◽  
Chenming Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Cellular Networks ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Allocation Problem ◽  
D2d Communications ◽  
Improved Harmony Search ◽  
Improved Harmony Search Algorithm

In this paper, the resource allocation problem for device-to-device (D2D) communications underlaying cellular networks is formulated and analyzed. In our scenario, we consider that the number of D2D user equipment (DUE) pairs is far larger than that of cellular user equipments (CUEs). Meanwhile, the resource blocks are divided into two types: resource blocks for CUEs and the ones for CUEs and DUEs. Firstly, the system model is presented, and the resource allocation problem is formulated. Then, a resource allocation scheme based on the genetic algorithm is proposed. To overcome the problem that the dedicated resource is not fully shared in the genetic algorithm, an improved harmony search algorithm is proposed for resource allocation. Finally, the analysis and simulation results show that the performances of the proposed genetic algorithm and the improved harmony search algorithm outperform than that of the random algorithm and are very close to that of the exhaustive algorithm. This result can provide an effective optimization for resource allocation of D2D communications.

Sign in / Sign up

Export Citation Format

Share Document