Optimum Design of Cellular Beams Using the Harmony Search Method

2009 ◽  
Author(s):  
F. Erdal ◽  
M.P. Saka
Keyword(s):  
Optimum Design ◽  
Harmony Search ◽  
Search Method

Optimum Design of Reinforced Concrete Retaining Walls

2018 ◽  
pp. 360-378
Author(s):  
Rasim Temür ◽  
Gebrail Bekdaş
Keyword(s):  
Reinforced Concrete ◽  
Optimum Design ◽  
Search Algorithm ◽  
Harmony Search ◽  
Minimum Cost ◽  
Retaining Walls ◽  
Slab Thickness ◽  
Construction Costs ◽  
Design Variables ◽  
Cantilever Retaining Walls

Methodologies based on metaheuristic algorithms such as particle swarm optimization, harmony search algorithm, and teaching-learning-based optimization are proposed for optimum design of reinforced concrete cantilever retaining walls. The objective function of optimization is to find a design providing minimum cost, including material and construction costs. For this purpose, the best combination of 11 design variables (heel and toe projections, stem thickness at the top and bottom of a wall, slab thickness and rebar diameters, and spacing between the bars) that satisfy 29 design constraints including stability (overturning, sliding, and bearing) and reinforced concrete design of the wall are searched during the optimization process. The rules of ACI 318 14 (building code requirements for structural concrete) are used for the reinforced concrete design. In order to determine the strengths and weaknesses of algorithms, several different cases are investigated. As conclusions, some suggestions have been obtained that will lead to future work in this field.

Hybrid system approach to optimum design of a ship

1999 ◽  
Vol 13 (1) ◽  
pp. 1-11 ◽  
Author(s):  
DONGKON LEE
Keyword(s):  
Optimum Design ◽  
Hybrid System ◽  
Optimization Algorithm ◽  
Search Method ◽  
Design Stage ◽  
Search Efficiency ◽  
Knowledge Based System ◽  
Initial Design ◽  
Optimum Point ◽  
Knowledge Based

To obtain optimal design efficiently in the initial design stage of a ship, a hybrid system is developed by integrating the optimization algorithm and knowledge-based system. The hybrid system can manipulate numeric and symbolic data simultaneously. To increase search efficiency in a design space, the optimization algorithm (optimizer) is implemented by coupling a genetic algorithm (GA) and search method. The optimizer determines a candidate region around the optimum point by using the GA, then searches the optimum point by the search method concentrating in this region, thus reducing calculation time and increasing search efficiency. To generate input data for the optimizer, a rule-based system is developed. Some domain knowledge for ship optimization in the initial design stage is retrieved from a database of existing ship and design experts. The obtained knowledge is stored in the knowledge base. The optimizer incorporates a knowledge-based system with heuristic and analytic knowledge, thereby narrowing the feasible space of the design variables. Therefore, search speed and the capability of finding an optimum point will be increased in comparison with conventional approach. The developed system is applied principally to particulars of optimization of ships with multicriteria. Through application ship design, it shows that the hybrid system can be a useful tool for optimum design.

Femur Bone Volumetric Estimation for Osteoporosis Classification Using Optimization-Based Deep Belief Network in X-Ray Images

2019 ◽  
Vol 62 (11) ◽  
pp. 1656-1670
Author(s):  
N Shankar ◽  
S Sathish Babu ◽  
C Viswanathan
Keyword(s):  
Gradient Descent ◽  
Harmony Search ◽  
Search Method ◽  
Deep Belief Network ◽  
Shape Model ◽  
Belief Network ◽  
Femur Bone ◽  
Volumetric Estimation ◽  
Real Time Database ◽  
Improved Accuracy

AbstractOsteoporosis classification is a significant requirement in the medical field to automatically classify the patients with skeleton disorder that occurs as a result of aging. The classification algorithms required improved accuracy and computationally less complexity. Accordingly, this paper proposes a classification method using the proposed gradient harmony search (GHS) optimization-based deep belief network. The GHS is developed by integrating the harmony search (HS) in the gradient descent (GD) algorithm. The osteoporosis classification is progressed as five major steps: preprocessing, segmentation using active shape model, geometric estimation using the proposed template search method, feature extraction for extracting the medical and image level features, and osteoporosis classification using the proposed GHS based deep belief network. The proposed template search method updates the geometric points of the femur segment effectively and automatically. Experimentation using the real-time database ensures the effectiveness of the proposed method in terms of accuracy, sensitivity, and specificity. The proposed method acquired the accuracy of 0.9539, proving that the osteoporosis classification using the proposed algorithm seems to be effective in taking accurate decisions regarding the patients.

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.

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