scholarly journals A Dynamic Adjusting Novel Global Harmony Search for Continuous Optimization Problems

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 337 ◽  
Author(s):  
Chui-Yu Chiu ◽  
Po-Chou Shih ◽  
Xuechao Li
Keyword(s):  
Optimization Problems ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Genetic Mutation ◽  
Dynamic Adjustment ◽  
Mutation Probability ◽  
Adjustment Strategy ◽  
Improved Harmony Search ◽  
Searching Ability ◽  
Continuous Optimization Problems

A novel global harmony search (NGHS) algorithm, as proposed in 2010, is an improved algorithm that combines the harmony search (HS), particle swarm optimization (PSO), and a genetic algorithm (GA). Moreover, the fixed parameter of mutation probability was used in the NGHS algorithm. However, appropriate parameters can enhance the searching ability of a metaheuristic algorithm, and their importance has been described in many studies. Inspired by the adjustment strategy of the improved harmony search (IHS) algorithm, a dynamic adjusting novel global harmony search (DANGHS) algorithm, which combines NGHS and dynamic adjustment strategies for genetic mutation probability, is introduced in this paper. Moreover, extensive computational experiments and comparisons are carried out for 14 benchmark continuous optimization problems. The results show that the proposed DANGHS algorithm has better performance in comparison with other HS algorithms in most problems. In addition, the proposed algorithm is more efficient than previous methods. Finally, different strategies are suitable for different situations. Among these strategies, the most interesting and exciting strategy is the periodic dynamic adjustment strategy. For a specific problem, the periodic dynamic adjustment strategy could have better performance in comparison with other decreasing or increasing strategies. These results inspire us to further investigate this kind of periodic dynamic adjustment strategy in future experiments.

scholarly journals Using Dynamic Adjusting NGHS-ANN for Predicting the Recidivism Rate of Commuted Prisoners

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1187 ◽  
Author(s):  
Po-Chou Shih ◽  
Chui-Yu Chiu ◽  
Chi-Hsun Chou
Keyword(s):  
Harmony Search ◽  
Dynamic Parameter ◽  
Genetic Mutation ◽  
Recidivism Rate ◽  
Dynamic Adjustment ◽  
Parameter Adjustment ◽  
Forecasting System ◽  
Adjustment Strategies ◽  
Searching Ability ◽  
Dynamic Parameter Adjustment

Commutation is a judicial policy that is implemented in most countries. The recidivism rate of commuted prisoners directly affects people’s perceptions and trust of commutation. Hence, if the recidivism rate of a commuted prisoner could be accurately predicted before the person returns to society, the number of reoffences could be reduced; thereby, enhancing trust in the process. Therefore, it is of considerable importance that the recidivism rates of commuted prisoners are accurately predicted. The dynamic adjusting novel global harmony search (DANGHS) algorithm, as proposed in 2018, is an improved algorithm that combines dynamic parameter adjustment strategies and the novel global harmony search (NGHS). The DANGHS algorithm improves the searching ability of the NGHS algorithm by using dynamic adjustment strategies for genetic mutation probability. In this paper, we combined the DANGHS algorithm and an artificial neural network (ANN) into a DANGHS-ANN forecasting system to predict the recidivism rate of commuted prisoners. To verify the prediction performance of the DANGHS-ANN algorithm, we compared the experimental results with five other forecasting systems. The results showed that the proposed DANGHS-ANN algorithm gave more accurate predictions. In addition, the use of the threshold linear posterior decreasing strategy with the DANGHS-ANN forecasting system resulted in more accurate predictions of recidivism. Finally, the metaheuristic algorithm performs better searches with the dynamic parameter adjustment strategy than without it.

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.

An Efficient Hybrid Algorithm Based on HS and SFLA

2016 ◽  
Vol 30 (05) ◽  
pp. 1659012 ◽  
Author(s):  
Aijia Ouyang ◽  
Xuyu Peng ◽  
Yanbin Liu ◽  
Lilue Fan ◽  
Kenli Li
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Harmony Search ◽  
Complex Function ◽  
Continuous Optimization ◽  
Function Optimization ◽  
Local Optima ◽  
Hybrid Mechanism ◽  
Complex Functions ◽  
Continuous Optimization Problems

When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence precision. To overcome such shortcomings, a hybrid mechanism of selective search by combining HS algorithm and SFLA algorithm is as well proposed. An HS-SFLA algorithm is designed by taking the advantages of HS and SFLA algorithms. The hybrid algorithm of HS-SFLA is adopted for dealing with complex function optimization problems, the experimental results show that HS-SFLA outperforms other state-of-the-art intelligence algorithms significantly in terms of global search ability, convergence speed and robustness on 80% of the benchmark functions tested. The HS-SFLA algorithm could directly be applied to all kinds of continuous optimization problems in the real world.

scholarly journals An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

2020 ◽  
Vol 10 (6) ◽  
pp. 1910 ◽  
Author(s):  
Hui Li ◽  
Po-Chou Shih ◽  
Xizhao Zhou ◽  
Chunming Ye ◽  
Li Huang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Harmony Search Algorithm ◽  
Experimental Results ◽  
Metaheuristic Algorithms ◽  
Engineering Problems ◽  
New Harmony ◽  
Continuous Optimization Problems

The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.

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