scholarly journals A New Method for Analyzing the Performance of the Harmony Search Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1421 ◽  
Author(s):  
Shouheng Tuo ◽  
Zong Woo Geem ◽  
Jin Hee Yoon
Keyword(s):  
Success Rate ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
High Dimensional ◽  
Multimodal Optimization ◽  
Harmony Memory ◽  
Dimensional Optimization ◽  
Take All

A harmony search (HS) algorithm for solving high-dimensional multimodal optimization problems (named DIHS) was proposed in 2015 and showed good performance, in which a dynamic-dimensionality-reduction strategy is employed to maintain a high update success rate of harmony memory (HM). However, an extreme assumption was adopted in the DIHS that is not reasonable, and its analysis for the update success rate is not sufficiently accurate. In this study, we reanalyzed the update success rate of HS and now present a more valid method for analyzing the update success rate of HS. In the new analysis, take-k and take-all strategies that are employed to generate new solutions are compared to the update success rate, and the average convergence rate of algorithms is also analyzed. The experimental results demonstrate that the HS based on the take-k strategy is efficient and effective at solving some complex high-dimensional optimization problems.

A harmony search algorithm for high-dimensional multimodal optimization problems

2015 ◽  
Vol 46 ◽  
pp. 151-163 ◽  
Author(s):  
Shouheng Tuo ◽  
Junying Zhang ◽  
Longquan Yong ◽  
Xiguo Yuan ◽  
Baobao Liu ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
High Dimensional ◽  
Multimodal Optimization

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).

scholarly journals A Cooperative Harmony Search Algorithm for Function Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Gang Li ◽  
Qingzhong Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cooperative Behavior ◽  
Optimization Technique ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization ◽  
Solution Vector ◽  
Original Algorithm ◽  
Harmony Memory

Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs) and particle swarm optimizers (PSOs). It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS) is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.

Opposition-Based Improved Harmony Search Algorithm Solve Unconstrained Optimization Problems

2013 ◽  
Vol 365-366 ◽  
pp. 170-173
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang ◽  
Li Qun Gao
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Solution Quality ◽  
Improved Harmony Search ◽  
Improved Harmony Search Algorithm ◽  
Harmony Memory

This paper develops an opposition-based improved harmony search algorithm (OIHS) for solving global continuous optimization problems. The proposed method is different from the classical harmony search (HS) in three aspects. Firstly, the candidate harmony is randomly chosen from the harmony memory or opposition harmony memory was generated by opposition-based learning, which enlarged the algorithm search space. Secondly, two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are adjusted dynamically with respect to the evolution of the search process. Numerical results demonstrate that the proposed algorithm performs much better than the existing HS variants in terms of the solution quality and the stability.

A Self-Study Harmony Search Algorithm for Optimization Problems

2014 ◽  
Vol 1006-1007 ◽  
pp. 1017-1020
Author(s):  
Ping Zhang ◽  
Mei Ling Li ◽  
Qian Han ◽  
Guo Jun Li
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Unconstrained Optimization Problems ◽  
Self Study ◽  
Harmony Memory

A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS)

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.

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