scholarly journals Applications of Harmony Search Algorithm

10.29007/pvrk ◽  
2018 ◽  
Author(s):  
Gunjan Chauhan ◽  
Vishal Patel ◽  
Vishal Arekar
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Mathematical Optimization ◽  
Visual Basic ◽  
Harmony Search Algorithm ◽  
Particle Swarm Algorithm ◽  
Decision Variables ◽  
Optimum Solution

Harmony search Algorithm (HSA) is mostly preferred for solving optimizationproblems and gives optimum solution of the problems. . It is based on improvisation of harmony in music process where musicians improvise their instruments’ pitch by searching for a aesthetically pleasant harmony. As the musicians in improvisation process try to find the best harmony in terms of aesthetics, the decision variables in optimization process try to be the best solution in terms of objective function. In the present work, the harmony search method is studied with an attempt to use it to solve various structural optimization problems. Harmony search can be more effective than some of the optimization available right now like genetic algorithms, particle swarm algorithm, ant colony algorithm, gravity search algorithm etc. The programming language used in this work is Visual Basic-macro excel. The programs for harmony search algorithm is developed in macro and their reliability is checked by verifying it with various mathematical optimization problems.

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.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

An Improved Novel Global Harmony Search Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2169-2172
Author(s):  
Zhi Kong ◽  
Guo Dong Zhang ◽  
Li Fu Wang
Keyword(s):  
Convergence Rate ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Global Optimum ◽  
Harmony Search Algorithm ◽  
Test Functions ◽  
Benchmark Test ◽  
Novel Method ◽  
Benchmark Test Functions

This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

Hybrid Harmony Search Algorithm for Global Optimization Problems

2014 ◽  
Vol 1065-1069 ◽  
pp. 3438-3441
Author(s):  
Guo Jun Li
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Search Method ◽  
Local Optima ◽  
Population Based Algorithm ◽  
Better Than

Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

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.

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