A New Variant Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2013 ◽  
Vol 365-366 ◽  
pp. 174-177
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Key Parameter

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to modify the optimization performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant integrate the position updating of the particle swarm optimization algorithm with pitch adjustment operation, and dynamically adjust the key parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks are to be tested. The numerical results demonstrated the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

Improved Harmony Search Algorithm with Crossover Operation

2013 ◽  
Vol 415 ◽  
pp. 353-356 ◽  
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Improved Harmony Search ◽  
Improved Harmony Search Algorithm

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant introduce a new crossover operation into HS, and design a strategy to adjust parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks carried out to be tested. The numerical results demonstrated that the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

A New Variant Harmony Search Algorithm with Random Mutation Strategy

2014 ◽  
Vol 1065-1069 ◽  
pp. 3425-3428
Author(s):  
Xiu Hong Zhao
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Random Mutation ◽  
Local Optima ◽  
Intelligent Optimization ◽  
New Variant ◽  
Mutation Strategy

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method, which has been paid much attention recently. However, intelligent optimization methods are easily trapped into local optima, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm with random mutation strategy (HSRM) is proposed in this paper. The HSRM uses a random mutation strategy to replace the pitch adjusting operation, and dynamically adjust the key parameter pitch adjusting rate (PAR). Experiment results demonstrated that the proposed method is superior to the HS and recently developed variants (IHS, and GHS) and other meta-heuristic algorithm.

Modified Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2528-2531
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Perfect State ◽  
Evolution Algorithm

Harmony search algorithm is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments’ pitches searching for a perfect state of harmony. To enable the harmony search algorithm to transcend its limited capability of local optimum, a modified harmony search algorithm is proposed in this paper. In the modified harmony search algorithm, the mutation operation of differential evolution algorithm is introduced into MHS algorithm, which improves its convergence. Several standard benchmark optimization functions are to be test and compare the performance of the MHS. The results revealed the superiority of the proposed method to the HS and recently developed variants.

scholarly journals An Elite Decision Making Harmony Search Algorithm for Optimization Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Lipu Zhang ◽  
Yinghong Xu ◽  
Yousong Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Solution Vector ◽  
Integer Variables ◽  
Design Variables ◽  
New Variant

This paper describes a new variant of harmony search algorithm which is inspired by a well-known item “elite decision making.” In the new algorithm, the good information captured in the current global best and the second best solutions can be well utilized to generate new solutions, following some probability rule. The generated new solution vector replaces the worst solution in the solution set, only if its fitness is better than that of the worst solution. The generating and updating steps and repeated until the near-optimal solution vector is obtained. Extensive computational comparisons are carried out by employing various standard benchmark optimization problems, including continuous design variables and integer variables minimization problems from the literature. The computational results show that the proposed new algorithm is competitive in finding solutions with the state-of-the-art harmony search variants.

Discrete-Continuous Configuration Optimization Methods for Structures Using the Harmony Search Algorithm

2006 ◽  
Vol 324-325 ◽  
pp. 1293-1296 ◽  
Author(s):  
K.S. Lee ◽  
Chang Sik Choi
Keyword(s):  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimization Methods ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Gradient Search ◽  
Geometric Variables ◽  
Derivative Information

This paper proposes an efficient structural optimization methods based on the harmony search (HS) heuristic algorithm that treat integrated discrete sizing and continuous geometric variables. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so derivative information is unnecessary. A benchmark truss example is presented to demonstrate the effectiveness and robustness of the new method, as compared to current optimization methods. The numerical results reveal that the proposed method is a powerful search and design optimization technique for structures with discrete member sizes, and may yield better solutions than those obtained using current methods.

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.

An Amended Harmony Search Algorithm for Unconstrained Optimization Problems

2012 ◽  
Vol 190-191 ◽  
pp. 911-914
Author(s):  
Ruo Ping Li ◽  
Hai Bin Ouyang ◽  
Li Qun Gao
Keyword(s):  
Convergence Rate ◽  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Local Optima ◽  
Unconstrained Optimization Problems ◽  
Solution Accuracy

Harmony search (HS) algorithm is a new mate heuristic algorithm, which is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Its own potential and shortage, one of its main disadvantages is that it easily trapped into local optima and converges very slowly. Based on the conception of swarm intelligence, this paper presents an amended harmony search (AHS) algorithm. AHS introduces a novel position updating strategy for generating new solution vectors, which enhances solution accuracy and convergence rate of algorithm. Several standard benchmark optimization functions are to be test and compare the performance of the AHS. The results revealed the superiority of the proposed method to the HS and its three improved algorithms (IHS, GHS and NGHS).

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.

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.

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