Hybrid differential evolution harmony search algorithm for numerical optimization problems

2013 ◽  
Author(s):  
Zhaohua Cui ◽  
Liqun Gao ◽  
Haibin Ouyang ◽  
Hongjun Li
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm

A Modified Harmony Search Algorithm for Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2532-2535
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
The Other ◽  
Self Learning ◽  
Harmony Memory

This paper presents a modified harmony search (MHS) algorithm for solving numerical optimization problems. MHS employs a novel self-learning strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In the proposed MHS algorithm, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. The other two key parameters PAR and bw adjust dynamically with generation number. Based on a large number of experiments, MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and GHS).

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.

A Modified Harmony Search Algorithm Solve Numerical Optimization Problems

2014 ◽  
Vol 602-605 ◽  
pp. 3589-3592
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Local Development ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Test Functions ◽  
Optimal Value ◽  
Pitch Adjustment ◽  
Better Than

This paper proposes a new effective MHS algorithm to solve numerical optimization problems. The MHS algorithm first adopt a novel self-studying strategy, which makes it easy balance the global search ability and local development ability, prevent the MHS algorithm trapped into local optimal value. besides, the harmony memory consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth distance (bw) is changed with function values dynamically, it can effectively improve the convergence speed and precision of the algorithm Based on five test functions , experiments results obtained by the MHS algorithm are better than those obtained using HS, IHS and NGHS algorithm in the literature.

Hybridizing Harmony Search Algorithm with Multi-Parent Crossover to Solve Real World Optimization Problems

2013 ◽  
Vol 4 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Iyad Abu Doush ◽  
Faisal Alkhateeb ◽  
Eslam Al Maghayreh ◽  
Mohammed Azmi Al-Betar ◽  
Basima Hani F. Hasan
Keyword(s):  
Evolutionary Algorithm ◽  
Real World ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Experimental Results ◽  
Test Problems ◽  
Harmony Memory

Harmony search algorithm (HSA) is a recent evolutionary algorithm used to solve several optimization problems. The algorithm mimics the improvisation behaviour of a group of musicians to find a good harmony. Several variations of HSA have been proposed to enhance its performance. In this paper, a new variation of HSA that uses multi-parent crossover is proposed (HSA-MPC). In this technique three harmonies are used to generate three new harmonies that will replace the worst three solution vectors in the harmony memory (HM). The algorithm has been applied to solve a set of eight real world numerical optimization problems (1-8) introduced for IEEE-CEC2011 evolutionary algorithm competition. The experimental results of the proposed algorithm are compared with the original HSA, and two variations of HSA: global best HSA and tournament HSA. The HSA-MPC almost always shows superiority on all test problems.

scholarly journals Enhancing the Harmony Search Algorithm Performance on Constrained Numerical Optimization

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 25759-25780 ◽  
Author(s):  
Edgar Alfredo Portilla-Flores ◽  
Alvaro Sanchez-Marquez ◽  
Leticia Flores-Pulido ◽  
Eduardo Vega-Alvarado ◽  
Maria Barbara Calva Yanez ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Algorithm Performance

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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