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

Improved Global Harmony Search Algorithm for Numerical Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 2295-2298
Author(s):  
Ping Zhang ◽  
Mei Ling Li ◽  
Qian Han ◽  
Yi Ning Zhang ◽  
Guo Jun Li
Keyword(s):  
Convergence Rate ◽  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Recent Literature ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Convergence And Stability ◽  
Benchmark Experiment ◽  
Pitch Adjustment

To intend to improve the optimization performance of harmony search (HS) algorithm, an improved global harmony search (IGHS) algorithm was presented in this paper. In this algorithm, inspired by swarm intelligence, the global best harmony are borrowed to enhance the optimization accuracy of HS; and mutation and crossover operation instead of pitch adjustment operation to improved the algorithm convergence rate. The key parameters are adjusted to balance the local and global search. Several benchmark experiment simulations, the IGHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its other three improved algorithms (IHS, GHS and SGHS) that reported in recent literature.

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).

Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

2014 ◽  
Vol 20 (1) ◽  
pp. 273-285 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Xiangjun Zhao ◽  
Hai Cheng Eric Chu
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Harmony Search Algorithm ◽  
Global Numerical Optimization

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.

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