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.

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2013 ◽  
Vol 365-366 ◽  
pp. 182-185
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Convergence And Stability ◽  
Update Strategy ◽  
Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

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.

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

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 Differential Evolution Harmony Search Algorithm for Power Economic Dispatch Problems

2013 ◽  
Vol 415 ◽  
pp. 345-348
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Recent Literature ◽  
Search Algorithm ◽  
Economic Dispatch ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
System Experiment ◽  
Mutagenic Factor ◽  
Key Parameter ◽  
Power Economic

In this paper, a hybrid differential evolution harmony search (HDEHS) algorithm was presented for solving power economic dispatch problems. In this algorithm, mutation and crossover operation instead of harmony memory consideration and pitch adjustment operation, this improved the algorithm convergence rate. Moreover, dynamically adjust the key parameter (e.g. mutagenic factor F, crossover rate CR) to balance the local and global search. Based on a 13 units power system experiment simulations, the HDEHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its three improved algorithms (IHS, GHS and NGHS) 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).

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