Opposition-Based Learning Harmony Search Algorithm Solving Unconstrained Optimization Problems

2014 ◽  
Vol 1006-1007 ◽  
pp. 1035-1038
Author(s):  
Ping Zhang ◽  
Peng Sun ◽  
Guo Jun Li
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Solution Quality ◽  
Opposition Based Learning ◽  
Unconstrained Optimization Problems ◽  
The Stability

Recently, a new meta-heuristic optimization algorithm–harmony search (HS) was developed,which imitates the behaviors of music improvisation. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to enhance diversity and prevent it trapped into local optimal. This paper develops an opposition-based learning harmony search algorithm (OLHS) for solving unconstrained optimization problems. The proposed method uses the best harmony to play pitch adjustment, and bring the concept of opposition-base learning into improvisation, which enlarged the algorithm search space. Besides, we design a new parameter setting strategy to directly tune the parameters in the search process, and balance the process of exploitation and exploration. 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-Adaptive Harmony Search Algorithm for Unconstrained Optimization Problems

2014 ◽  
Vol 596 ◽  
pp. 192-195
Author(s):  
Ping Zhang ◽  
Peng Sun ◽  
Yi Ning Zhang ◽  
Guo Jun Li
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Parameter Setting ◽  
Unconstrained Optimization Problems ◽  
Suitable Parameter ◽  
Parameter Values ◽  
Self Adaptive

Recently, a new meta-heuristic optimization algorithm–harmony search (HS) was developed, which imitates the behaviors of music improvisation. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to select suitable parameter values. In this paper, a self-adaptive harmony search algorithm (SaHS) proposed. In this algorithm, we design a new parameter setting strategy to directly tune the parameters in the search process, and balance the process of exploitation and exploration. Finally, we use SaHS to solve unconstrained optimization problems so as to profoundly study and analyze the performance of the SaHS. The results show that the SaHS has better convergence accuracy than the other three harmony search algorithms.

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.

scholarly journals A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype

2021 ◽  
Vol 11 (19) ◽  
pp. 8931
Author(s):  
Daniel Molina-Pérez ◽  
Edgar Alfredo Portilla-Flores ◽  
Eduardo Vega-Alvarado ◽  
Maria Bárbara Calva-Yañez ◽  
Gabriel Sepúlveda-Cervantes
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Fronts ◽  
Exploration Exploitation ◽  
Pitch Adjustment

In this work, a new version of the Harmony Search algorithm for solving multi-objective optimization problems is proposed, MOHSg, with pitch adjustment using genotype. The main contribution consists of adjusting the pitch using the crowding distance by genotype; that is, the distancing in the search space. This adjustment automatically regulates the exploration–exploitation balance of the algorithm, based on the distribution of the harmonies in the search space during the formation of Pareto fronts. Therefore, MOHSg only requires the presetting of the harmony memory accepting rate and pitch adjustment rate for its operation, avoiding the use of a static bandwidth or dynamic parameters. MOHSg was tested through the execution of diverse test functions, and it was able to produce results similar or better than those generated by algorithms that constitute search variants of harmonies, representative of the state-of-the-art in multi-objective optimization with HS.

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)

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).

A Non-Monotone Line Search Combination Technique for Unconstrained Optimization

2014 ◽  
Vol 8 (1) ◽  
pp. 218-221 ◽  
Author(s):  
Ping Hu ◽  
Zong-yao Wang
Keyword(s):  
Global Convergence ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Combination ◽  
Combination Rule ◽  
Combination Technique ◽  
Unconstrained Optimization Problems

We propose a non-monotone line search combination rule for unconstrained optimization problems, the corresponding non-monotone search algorithm is established and its global convergence can be proved. Finally, we use some numerical experiments to illustrate the new combination of non-monotone search algorithm’s effectiveness.

scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

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