scholarly journals Comparison of Genetic Algorithm and Harmony Search Method for 2D Geometry Optimization.

2018 ◽  
Vol 159 ◽  
pp. 01009 ◽  
Author(s):  
Mohammad Ghozi ◽  
Anik Budiati
Keyword(s):  
Genetic Algorithm ◽  
Harmony Search ◽  
Geometry Optimization ◽  
Steel Structure ◽  
Search Method ◽  
Geometric Optimization ◽  
Computational Time ◽  
Time Required ◽  
Average Computational Time ◽  
Better Than

There are many applications of Genetic Algorithm (GA) and Harmony Search (HS) Method for solving problems in civil engineering design. The question is, still, which method is better for geometry optimization of a steel structure. The purpose of this paper is to compare GA and HS performance for geometric optimization of a steel structure. This problem is solved by optimizing a steel structure using GA and HS and then comparing the structure’s weight as well as the time required for the calculation. In this study, GA produced a structural weight of 2308.00 kg to 2387.00 kg and HS scored 2193.12 kg to 2239.48 kg. The average computational time required by GA is 607 seconds and HS needed 278 seconds. It concludes that HS is faster and better than GA for geometry optimization of a steel structure.

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).

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

3D-Multi Constrained Heterogeneous Packing Using Genetic Approach

2012 ◽  
Vol 479-481 ◽  
pp. 1825-1830 ◽  
Author(s):  
Rajesh Kanna ◽  
P. Malliga ◽  
K. Sarukesi
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Three Dimensional ◽  
Empty Space ◽  
Computational Time ◽  
Orientation Tuning ◽  
Optimal Position ◽  
Different Shapes ◽  
Practical Constraints ◽  
Better Than

This paper presents a combination of Genetic Algorithm (GA) and Tuning algorithm, for optimizing Three Dimensional (3D) arbitrary sized heterogeneous box packing into a container, by considering practical constraints facing in the logistics industries. Objective of this research is to pack four different shapes of boxes of varying sizes into a container of standard dimension, without violating various practical constraints. Inorder to obtain a real time feasible packing pattern, Genetic Algorithm is developed to maximize the container volume utilization and inturn profit [9]. It significantly improves the search efficiency with less computational time and loads most of the heterogeneous boxes into the container by considering its optimal position and orientation. Tuning algorithm is used to decode the genetic output into user understandable sequential packing pattern and to fill the left-out empty space inside the container. In general, GA in conjunction with the tuning algorithm is substantially better than those obtained by applying heuristics to the bin packing directly.

scholarly journals An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2287
Author(s):  
Ruba Obiedat ◽  
Laila Al-Qaisi ◽  
Raneem Qaddoura ◽  
Osama Harfoushi ◽  
Ala’ M. Al-Zoubi
Keyword(s):  
Nearest Neighbor ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Word Embedding ◽  
Computational Time ◽  
K Nearest Neighbor ◽  
Classification Techniques ◽  
Hand Sanitizer ◽  
Time Required

Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The experiments are conducted on five different datasets including medical gloves, hand sanitizer, medical oxygen, face masks, and a combination of all these datasets. The results show that the harmony search algorithm successfully reduced the number of features by 94.25%, 89.5%, 89.25%, 92.5%, and 84.25% for the medical glove, hand sanitizer, medical oxygen, face masks, and whole datasets, respectively, while keeping a competitive performance in terms of accuracy and root mean square error (RMSE) for the classification techniques and decreasing the computational time required for classification.

scholarly journals Embedding quasi-static time series within a genetic algorithm for stochastic optimization: the case of reactive power compensation on distribution systems

2020 ◽  
Vol 7 (2) ◽  
pp. 177-194
Author(s):  
Juan M Lujano-Rojas ◽  
Ghassan Zubi ◽  
Rodolfo Dufo-López ◽  
José L Bernal-Agustín ◽  
José L Atencio-Guerra ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Distribution System ◽  
Distribution Systems ◽  
Reactive Power ◽  
Optimization Method ◽  
Reactive Power Compensation ◽  
Optimal Placement ◽  
Computational Time ◽  
Time Required

Abstract This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required.

An Efficient Hybrid Based on HS and GA Solving Blocking Flow Shop Scheduling Problems

2012 ◽  
Vol 479-481 ◽  
pp. 1893-1896
Author(s):  
Yun Bao ◽  
Liping Zheng ◽  
Hua Jiang
Keyword(s):  
Genetic Algorithm ◽  
Computational Result ◽  
Flow Shop ◽  
Harmony Search ◽  
Hybrid Algorithms ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Better Than

This paper presents an efficient hybrid algorithms (EHA) based on harmony search (HS) algorithms and genetic algorithm (GA) for solving blocking flow shop scheduling problem. An improved GA is used to get better results. The computational result shows that EHA is not only better than GA , but also better than HS algorithm.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

scholarly journals Green–Haar wavelets method for generalized fractional differential equations

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Mujeeb ur Rehman ◽  
Dumitru Baleanu ◽  
Jehad Alzabut ◽  
Muhammad Ismail ◽  
Umer Saeed
Keyword(s):  
Differential Equations ◽  
Fractional Differential Equations ◽  
Haar Wavelet ◽  
Haar Wavelets ◽  
Computational Time ◽  
Operational Matrices ◽  
Numerical Techniques ◽  
Fractional Differential ◽  
Fractional Boundary Value Problems ◽  
Better Than

Abstract The objective of this paper is to present two numerical techniques for solving generalized fractional differential equations. We develop Haar wavelets operational matrices to approximate the solution of generalized Caputo–Katugampola fractional differential equations. Moreover, we introduce Green–Haar approach for a family of generalized fractional boundary value problems and compare the method with the classical Haar wavelets technique. In the context of error analysis, an upper bound for error is established to show the convergence of the method. Results of numerical experiments have been documented in a tabular and graphical format to elaborate the accuracy and efficiency of addressed methods. Further, we conclude that accuracy-wise Green–Haar approach is better than the conventional Haar wavelets approach as it takes less computational time compared to the Haar wavelet method.

Global geometry optimization of (Ar)n and B(Ar)n clusters using a modified genetic algorithm

1996 ◽  
Vol 104 (7) ◽  
pp. 2684-2691 ◽  
Author(s):  
Susan K. Gregurick ◽  
Millard H. Alexander ◽  
Bernd Hartke
Keyword(s):  
Genetic Algorithm ◽  
Geometry Optimization ◽  
Global Geometry ◽  
Modified Genetic Algorithm

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

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