A two-phase harmony search algorithm for continuous optimization

2017 ◽  
Vol 33 (4) ◽  
pp. 1038-1075 ◽  
Author(s):  
Assif Assad ◽  
Kusum Deep
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Harmony Search Algorithm ◽  
Two Phase

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 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.

scholarly journals A Novel Discrete Global-Best Harmony Search Algorithm for Solving 0-1 Knapsack Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wan-li Xiang ◽  
Mei-qing An ◽  
Yin-zhen Li ◽  
Rui-chun He ◽  
Jing-fang Zhang
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Genetic Mutation ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Two Phase ◽  
Solution Quality ◽  
Selection Scheme ◽  
Binary Coding ◽  
New Harmony

In order to better solve discrete 0-1 knapsack problems, a novel global-best harmony search algorithm with binary coding, called DGHS, is proposed. First, an initialization based on a greedy mechanism is employed to improve the initial solution quality in DGHS. Next, we present a novel improvisation process based on intuitive cognition of improvising a new harmony, in which the best harmony of harmony memory (HM) is used to guide the searching direction of evolution during the process of memory consideration, or else a harmony is randomly chosen from HM and then a discrete genetic mutation is done with some probability during the phase of pitch adjustment. Third, a two-phase repair operator is employed to repair an infeasible harmony vector and to further improve a feasible solution. Last, a new selection scheme is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems.

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