scholarly journals Parallel algorithm of multiobjective optimization harmony search based on cloud computing

2017 ◽  
Vol 11 (3) ◽  
pp. 301-313 ◽  
Author(s):  
Wenjing Li ◽  
Wenhong Du ◽  
Weifeng Tang ◽  
Ying Pan ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Multiobjective Optimization ◽  
Parallel Algorithm ◽  
Search Algorithm ◽  
Harmony Search ◽  
Complex Function ◽  
Search Algorithms ◽  
Harmony Search Algorithm ◽  
Fine Tuning ◽  
Local Optimum

In order to solve the problems of traditional harmony search in complex function multiobjective optimization, such as low precision, slow convergence, and easy to fall into local optimum, this article proposes a multiobjective optimization harmony search parallel algorithm based on cloud computing. First, according to the characteristics that the traditional harmony search algorithm uses a single harmony library for storing and processing the memory harmony, and it is divided into multiple harmony sublibraries according to different harmony. At the same time, the roulette selection and dynamic trade-off factor strategies are used for the dynamic setting of harmony memory library value-taking probability, pitch fine-tuning probability, pitch fine-tuning bandwidth, and other parameters which the traditional harmony search algorithm mainly relies on. Then, MapReduce programming model is used to establish Map and Reduce core parallel computing functions, to construct the parallel algorithm of dynamic parameter harmony search based on cloud computing. Finally, the algorithm optimization comparison test is conducted on Hadoop platform and compared with several existing optimal harmony search algorithms, the searching precision of this algorithm is improved by eight orders of magnitude, and the iteration number on the convergence speed is reduced by 6500 times, and the parallel achieves the linear acceleration ratio. Experimental results show that the optimization efficiency of this algorithm is higher than several existing optimal harmony search algorithms.

scholarly journals OPTIMASI PENJADWALAN PRODUKSI MENGGUNAKAN PENDEKATAN ALGORITMA HARMONY SEARCH DI PT ADI SATRIA ABADI (ASA

2020 ◽  
Vol 11 (1) ◽  
pp. 7-12
Author(s):  
Inaaratul Chusna Ichda Purwanto ◽  
Yohanes Anton Nugroho ◽  
Suseno Suseno
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Search Algorithms ◽  
Harmony Search Algorithm ◽  
Due Date ◽  
Average Value ◽  
Scheduling Method ◽  
Time Savings ◽  
A Company ◽  
Selection Of

PT Adi Satria Abadi (ASA) is a company engaged in the processing of leather, especially sheep skin and goat skin, which is used for the manufacture of golf gloves. The problem faced by the company is the production process that exceeds the due date to other customers who order products at PT ASA. From the research, it is known that the cause is a company scheduling method that has not been organized so that the production sequence is concurrent. Selection of methods Harmony Search algorithms in scheduling are caused by delays. The Harmony Search algorithm can provide a better makespan value than the company method. The results of the company method obtain 0.9 months makespan average, the Harmony Search Algorithm method produces an average 0.8 months makespan. In addition, the use of the Harmony Search Algorithm method can reduce the average value of 0.1 months makespan. The results of the study in three months experienced time savings of 0.6 months, 0.6 months and 0.1 months respectively.

scholarly journals Automatic Calibration for CE-QUAL-W2 Model Using Improved Global-Best Harmony Search Algorithm

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2308
Author(s):  
Afshin Shabani ◽  
Xiaodong Zhang ◽  
Xuefeng Chu ◽  
Haochi Zheng
Keyword(s):  
Water Temperature ◽  
Mean Squared Error ◽  
Search Algorithm ◽  
Harmony Search ◽  
Pso Algorithm ◽  
Calibration Method ◽  
Harmony Search Algorithm ◽  
Automatic Calibration ◽  
Local Optimum ◽  
Calibration Methods

CE-QUAL-W2 is widely used for simulating hydrodynamics and water quality of the aquatic environments. Currently, the model calibration is mainly based on trial and error, and therefore it is subject to the knowledge and experience of users. The Particle Swarm Optimization (PSO) algorithm has been tested for automatic calibration of CE-QUAL-W2, but it has an issue of prematurely converging to a local optimum. In this study, we proposed an Improved Global-Best Harmony Search (IGHS) algorithm to automatically calibrate the CE-QUAL-W2 model to overcome these shortcomings. We tested the performance of the IGHS calibration method by simulating water temperature of Devils Lake, North Dakota, which agreed with field observations with R2 = 0.98, and RMSE = 1.23 and 0.77 °C for calibration (2008–2011) and validation (2011–2016) periods, respectively. The same comparison, but with the PSO-calibrated CE-QUAL-W2 model, produced R2 = 0.98 and Root Mean Squared Error (RMSE) = 1.33 and 0.91 °C. Between the two calibration methods, the CE-QUAL-W2 model calibrated by the IGHS method could lower the RMSE in water temperature simulation by approximately 7–15%.

A novel optimized cyclic part feeding system with line-integrated supermarkets

2018 ◽  
Vol 233 (3) ◽  
pp. 975-987 ◽  
Author(s):  
Binghai Zhou ◽  
Jiahui Xu
Keyword(s):  
Mixed Model ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Global Optimum ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Fleet Size ◽  
Plant Parts ◽  
Part Feeding

To unify the merits of traditional in-plant parts logistics alternatives such as line stocking and kitting, the concept of line-integrated supermarkets is introduced to improve the part feeding in mixed-model assembly lines. First, the highly interdependent optimization problems of assigning stations and scheduling logistics operators are described, and mathematical models are established with the aim to minimize the fleet size of logistics operators and unit part delivery time as well. Together with particular theorems and lemmas, a nested dynamic programming is presented to obtain global optimum for small-sized instances while a modified harmony search algorithm is constructed for medium- or large-sized instances. Benefit from repeatedly dividing and reconstructing the harmony memory, the computation speed is significantly enhanced. Meanwhile, crossover and mutation operations effectively improve the diversity of solutions to overcome deficiencies such as limited search depth and tendencies to trapping into local optimum. Finally, experimental results validate that the proposed algorithm is of competitive performance in effectiveness and efficiency compared to some other basic or modified meta-heuristics.

Harmony Search Algorithm with Opposition-Based Learning for Power System Economic Load Dispatch

2014 ◽  
Vol 1065-1069 ◽  
pp. 3434-3437
Author(s):  
Yi Ning Zhang
Keyword(s):  
Power System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Opposition Based Learning ◽  
Learning Techniques ◽  
Performance Of Algorithm ◽  
Better Than

A harmony search algorithm with opposition-based learning techniques (HS-OBL) to solve power system economic load dispatch has been presented. The proposed algorithm integrates the opposition-based learning operation with the improvisation process to prevent the HS-OBL algorithm from being trapped into the local optimum effectively. Besides, a new adjusting strategy is designed to dynamic adjust pitch adjusting rate (PAR) and harmony memory consideration rate (HMCR), which is to further improve the performance of algorithm. The HS-OBL is employed to solve 6 units and 13 units power system, the numerical results indicate that the HS-OBL has perform much better than harmony search(HS) algorithm and other improved algorithms that reported in recent literature.

scholarly journals Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
P. Sabarinath ◽  
M. R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Production Costs ◽  
Machining Parameters ◽  
Conflicting Objectives

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

Modified Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2528-2531
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Perfect State ◽  
Evolution Algorithm

Harmony search algorithm is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments’ pitches searching for a perfect state of harmony. To enable the harmony search algorithm to transcend its limited capability of local optimum, a modified harmony search algorithm is proposed in this paper. In the modified harmony search algorithm, the mutation operation of differential evolution algorithm is introduced into MHS algorithm, which improves its convergence. Several standard benchmark optimization functions are to be test and compare the performance of the MHS. The results revealed the superiority of the proposed method to the HS and recently developed variants.

Hybrid Harmony Search Algorithms

2015 ◽  
Vol 24 (06) ◽  
pp. 1530001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computational Cost ◽  
Population Based ◽  
Search Algorithms ◽  
Harmony Search Algorithm ◽  
Solution Quality

Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.

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