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.

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).

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.

New Harmony Search Algorithm for Mobile Robot Path Planning

2021 ◽  
pp. 3463-3473
Author(s):  
Yongbin Quan ◽  
Wenqi Wei ◽  
Haibin Ouyang ◽  
Xuejing Lan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Robot Path Planning ◽  
New Harmony ◽  
Robot Path

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.

scholarly journals Solving Cell Placement Problem Using Harmony Search Algorithms

2018 ◽  
Vol 8 (4) ◽  
pp. 3172-3176
Author(s):  
R. M. Al Qasem ◽  
S. M. Massadeh
Keyword(s):  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Placement Problem ◽  
Solution Quality ◽  
Chip Design ◽  
Cell Placement ◽  
Placement Algorithm

Cell placement is a phase in the chip design process, in which cells are assigned to physical locations. A placement algorithm is a way that satisfies the objectives and minimizes the total area while keeping enough space for routing. Cell placement is an NP-complete problem of very large size. In order to solve this problem, diversified heuristic algorithms are used. In this work, a new algorithm is proposed based on the harmony search algorithm. The harmony search algorithm mimics music improvisation process to find the optimal solution. Cell placement problem has many constraints, so in this work, the harmony search algorithm is modified to adapt to these constraints. Experiment results show that this algorithm is efficient for solving cell placement and is characterized by good performance, solution quality and likelihood of optimality.

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 Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 45-57
Author(s):  
Farah Aqilah Bohani ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Syaza Sharis ◽  
Rizuana Iqbal Hussain ◽  
Shahnorbanun Sahran ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Search Algorithm ◽  
Harmony Search ◽  
Brain Magnetic Resonance Imaging ◽  
Harmony Search Algorithm ◽  
Bees Algorithm ◽  
Tumor Segmentation ◽  
Multilevel Thresholding ◽  
Brain Tumor Segmentation ◽  
Solution Quality

Image segmentation of brain magnetic resonance imaging (MRI) plays a crucial role among radiologists in terms of diagnosing brain disease. Parts of the brain such as white matter, gray matter and cerebrospinal fluids (CFS), have to be clearly determined by the radiologist during the process of brain abnormalities detection. Manual segmentation is grueling and may be prone to error, which can in turn affect the result of the diagnosis. Nature inspired metaheuristic algorithms such as Harmony Search (HS), which was successfully applied in multilevel thresholding for brain tumor segmentation instead of the Patch-Levy Bees algorithm (PLBA). Even though the PLBA is one powerful multilevel thresholding, it has not been applied to brain tumor segmentation. This paper focuses on a comparative study of the PLBA and HS for brain tumor segmentation. The test dataset consisting of nine images was collected from the Tuanku Muhriz UKM Hospital (HCTM). As for the result, it shows that the PLBA has significantly outperformed HS. The performance of both algorithms is evaluated in terms of solution quality and stability.

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