scholarly journals A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

2020 ◽  
Vol 10 (8) ◽  
pp. 2916
Author(s):  
Xinchao Zhao ◽  
Rui Li ◽  
Junling Hao ◽  
Zhaohua Liu ◽  
Jianmei Yuan
Keyword(s):  
Global Optimization ◽  
State Of The Art ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Scaling Factor ◽  
Experimental Comparison ◽  
Beneficial Effects ◽  
Pitch Adjustment ◽  
Harmony Memory

The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.

Dynamic Adaptive Harmony Search Algorithm for Optimization

2011 ◽  
Vol 474-476 ◽  
pp. 1666-1671
Author(s):  
Yi Wen Wang ◽  
Min Yao
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Heuristic Optimization ◽  
Perfect State ◽  
Suitable Parameter ◽  
Improved Harmony Search ◽  
Parameter Values ◽  
Pitch Adjustment ◽  
Harmony Memory

A new meta-heuristic optimization algorithm–harmony search is conceptualized using the musical improvisation process of searching for a perfect state of harmony. 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, we proposed a novel algorithm to dynamically adapt the harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) and distance bandwidth (BW). The experimental results revealed the superiority of the proposed method to the original HS, improved harmony search (IHS) and global-best harmony search (GHS).

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).

Hybrid Harmony Search Algorithm for Global Optimization Problems

2014 ◽  
Vol 1065-1069 ◽  
pp. 3438-3441
Author(s):  
Guo Jun Li
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Search Method ◽  
Local Optima ◽  
Population Based Algorithm ◽  
Better Than

Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

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.

The Lot-Streaming Flow Scheduling Shops Based on a Hybrid Discrete Harmony Search Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 563-568
Author(s):  
Hong Yan Han
Keyword(s):  
Flow Shop ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Lot Streaming ◽  
Discrete Harmony Search ◽  
Harmony Memory ◽  
Streaming Flow

To solve the lot-streaming flow shop scheduling problem with the objective to minimize the total weighted earliness and tardiness, a hybrid discrete harmony search (HDHS) algorithm is proposed in this paper. Firstly, an effective harmony memory initialization approach is presented,an initial solution in harmony memory is generated by means of the famous NEH heuristic. Secondly, the HDHS algorithm utilizes an effective improvisation mechanism to generate new harmonies represented by job permutations. Lastly, the insert neighborhood search and swap operator are designed and embedded in the algorithm to enhance the local exploitation.Experimental results demonstrate the effectiveness of the proposed HDHS algorithms.

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