scholarly journals Novel Global Harmony Search Algorithm for Least Absolute Deviation

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Longquan Yong
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Convergence Property ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Least Squares Regression ◽  
Least Absolute Deviation ◽  
Absolute Deviation ◽  
Good Convergence ◽  
Nonsmooth Optimization Problem

The method of least absolute deviation (LAD) finds applications in many areas, due to its robustness compared to the least squares regression (LSR) method. LAD is robust in that it is resistant to outliers in the data. This may be helpful in studies where outliers may be ignored. Since LAD is nonsmooth optimization problem, this paper proposed a metaheuristics algorithm named novel global harmony search (NGHS) for solving. Numerical results show that the NGHS method has good convergence property and effective in solving LAD.

scholarly journals Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Longquan Yong ◽  
Sanyang Liu ◽  
Jianke Zhang ◽  
Quanxi Feng
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Population Diversity ◽  
Mutation Operator ◽  
Good Convergence ◽  
Improved Harmony Search ◽  
Mean And Variance ◽  
Evolution Algorithm

Harmony search (HS) method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE) is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization

2020 ◽  
Author(s):  
M Bidoki ◽  
M Fakhrahmad ◽  
M R Moosavi
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Text Summarization ◽  
Multi Objective Optimization ◽  
Extractive Summarization ◽  
Multi Objective ◽  
Optimization Task ◽  
Appropriate Sentences

Abstract Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

Improved Harmony Search Algorithm for Solving Optimization Problem with Mixed Discrete Variables

2013 ◽  
Vol 325-326 ◽  
pp. 1485-1488
Author(s):  
Shi Ming Hao ◽  
Li Zhi Cheng
Keyword(s):  
Constrained Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Decision Variables

The classical harmony search algorithm (HSA) can only be used to solve the unconstrained optimization problems with continuous decision variables. Therefore, the classical HSA is not suitable for solving an engineering optimization problem with mixed discrete variables. In order to improve the classical HSA, an engineering method for dealing with mixed discrete decision variables is introduced and an exact non-differentiable penalty function is used to transform the constrained optimization design model into an unconstrained mathematical model. Based on above improvements, a program of improved HSA is designed and it can be used for solving the constrained optimization design problems with continuous variables, integer variables and non-equidistant discrete variables. Finally, an optimization design example of single-stage cylindrical-gear reducer with mixed-discrete variables is given. The example shows that the designed program runs steadily and the proposed method is effective in engineering design.

scholarly journals Apple Image Recognition Multi-Objective Method Based on the Adaptive Harmony Search Algorithm with Simulation and Creation

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 180 ◽  
Author(s):  
Liqun Liu ◽  
Jiuyuan Huo
Keyword(s):  
Image Recognition ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Natural Scene ◽  
Multi Objective Optimization ◽  
Recognition Method ◽  
Multi Objective

Aiming at the low recognition effect of apple images captured in a natural scene, and the problem that the OTSU algorithm has a single threshold, lack of adaptability, easily caused noise interference, and over-segmentation, an apple image recognition multi-objective method based on the adaptive harmony search algorithm with simulation and creation is proposed in this paper. The new adaptive harmony search algorithm with simulation and creation expands the search space to maintain the diversity of the solution and accelerates the convergence of the algorithm. In the search process, the harmony tone simulation operator is used to make each harmony tone evolve towards the optimal harmony individual direction to ensure the global search ability of the algorithm. Despite no improvement in the evolution, the harmony tone creation operator is used to make each harmony tone to stay away from the current optimal harmony individual for extending the search space to maintain the diversity of solutions. The adaptive factor of the harmony tone was used to restrain random searching of the two operators to accelerate the convergence ability of the algorithm. The multi-objective optimization recognition method transforms the apple image recognition problem collected in the natural scene into a multi-objective optimization problem, and uses the new adaptive harmony search algorithm with simulation and creation as the image threshold search strategy. The maximum class variance and maximum entropy are chosen as the objective functions of the multi-objective optimization problem. Compared with HS, HIS, GHS, and SGHS algorithms, the experimental results showed that the improved algorithm has higher a convergence speed and accuracy, and maintains optimal performance in high-dimensional, large-scale harmony memory. The proposed multi-objective optimization recognition method obtains a set of non-dominated threshold solution sets, which is more flexible than the OTSU algorithm in the opportunity of threshold selection. The selected threshold has better adaptive characteristics and has good image segmentation results.

Harmony Search Algorithm and its Variants

2015 ◽  
Vol 29 (08) ◽  
pp. 1539001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
The Past

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

scholarly journals Design of a Wrist Rehabilitation System with a Novel Mixed Structural Optimization Applying Improved Harmony Search

2021 ◽  
Vol 11 (4) ◽  
pp. 1766
Author(s):  
Eduardo Vega-Alvarado ◽  
Valentín Vázquez-Castillo ◽  
Edgar Alfredo Portilla-Flores ◽  
Maria Bárbara Calva-Yañez ◽  
Gabriel Sepúlveda-Cervantes
Keyword(s):  
Latin American ◽  
Optimization Problem ◽  
Material Selection ◽  
Search Algorithm ◽  
Low Cost ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Novel Approach ◽  
Rehabilitation System ◽  
Improved Harmony Search

This paper presents the development of a wrist rehabilitation system with a novel approach for structural design, based on the modeling of an optimization problem solved by a metaheuristic algorithm, Improved Harmony Search (ImHS). It is part of a project for developing low-cost rehabilitation systems expressly designed for the population of Latin American countries. A mixed optimization problem is modeled for the design, where the material type is associated with an integer variable and the dimensions of the components are continuous parameters. The novelty is that each element is calculated individually, but considering the combined effect over the structure. The optimization works simultaneously on both the material selection and the meeting of the associated constraints, to guarantee that the system will not fail because of any load, neither it will be unsafe for the patients, since the operation will always be within the limits considered in the modeling. ImHS is a variant of the Harmony Search algorithm, modified to enhance the exploration and exploitation processes. It is a simple yet powerful metaheuristic, implemented in this development with additional modifications to handle constraints and mixed variables. The proposed approach produced quality results, indicating that ImHS can be applied to solve complex engineering problems, facilitating the manufacture and control processes.

scholarly journals Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983445 ◽  
Author(s):  
Yubin Miao ◽  
Fenglin Xu ◽  
Yanwei Hu ◽  
Jianping An ◽  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Motion Path ◽  
Motion Trajectory ◽  
Good Convergence ◽  
Radial Basis

The swing of the grab is a main factor affecting the working efficiency of overhead cranes. Thus, planning the optimal motion path can reduce the adverse effects caused by the grab swing and improve the loading and unloading efficiency. The dynamic model of the trolley–grab system is established by considering factors like the change of rope length, wind load, and air resistance. First, the radial basis function neural network is applied to generate a feasible motion trajectory of the crane trolley. Taking the swing angle and angular velocity of the grab at the discharge point as evaluation, the harmony search algorithm is then applied to optimize the neural network parameters and obtain the optimal anti-swing motion trajectory. The numerical simulation and practical testing results show that the harmony search–radial basis function algorithm generates a smooth motion trajectory with good convergence, achieving anti-swing control of the trolley–grab system.

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