A Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2012 ◽  
Author(s):  
Ping Zhang ◽  
Xiaoyou Shan ◽  
Wu Gu
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems

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).

A simplified binary harmony search algorithm for large scale 0–1 knapsack problems

2015 ◽  
Vol 42 (12) ◽  
pp. 5337-5355 ◽  
Author(s):  
Xiangyong Kong ◽  
Liqun Gao ◽  
Haibin Ouyang ◽  
Steven Li
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems

scholarly journals A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Shouheng Tuo ◽  
Longquan Yong ◽  
Fang’an Deng
Keyword(s):  
Learning Strategies ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Procedure ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Random Mutation ◽  
Teaching Learning

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

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.

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

2013 ◽  
Vol 32 (9) ◽  
pp. 2412-2417
Author(s):  
Yue-hong LI ◽  
Pin WAN ◽  
Yong-hua WANG ◽  
Jian YANG ◽  
Qin DENG
Keyword(s):  
Spectrum Sensing ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Linear
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