Development and Research of the Hybrid Approach to the Solution of Optimization Design Problems

2018 ◽  
pp. 246-257
Author(s):  
Leonid A. Gladkov ◽  
Nadezhda V. Gladkova ◽  
Sergey N. Leiba ◽  
Nikolay E. Strakhov
Keyword(s):  
Optimization Design ◽  
Hybrid Approach ◽  
Design Problems

Optimization Design of Electromagnetic Devices Using an Enhanced Salp Swarm Algorithm

2021 ◽  
Vol 35 (12) ◽  
pp. 1471-1476
Author(s):  
Houssem Bouchekara ◽  
Mostafa Smail ◽  
Mohamed Javaid ◽  
Sami Shamsah
Keyword(s):  
Optimization Design ◽  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Design Problems ◽  
Salp Swarm Algorithm ◽  
Electromagnetic Devices ◽  
Swarm Algorithm ◽  
Better Than

An Enhanced version of the Salp Swarm Algorithm (SSA) referred to as (ESSA) is proposed in this paper for the optimization design of electromagnetic devices. The ESSA has the same structure as of the SSA with some modifications in order to enhance its performance for the optimization design of EMDs. In the ESSA, the leader salp does not move around the best position with a fraction of the distance between the lower and upper bounds as in the SAA; rather, a modified mechanism is used. The performance of the proposed algorithm is tested on the widely used Loney’s solenoid and TEAM Workshop Problem 22 design problems. The obtained results show that the proposed algorithm is much better than the initial one. Furthermore, a comparison with other well-known algorithms revealed that the proposed algorithm is very competitive for the optimization design of electromagnetic devices.

Geometric Optimization of Radiant Enclosures Containing Specular Surfaces

2003 ◽  
Vol 125 (5) ◽  
pp. 845-851 ◽  
Author(s):  
K. J. Daun ◽  
D. P. Morton ◽  
J. R. Howell
Keyword(s):  
Design Methodology ◽  
Optimization Design ◽  
Numerical Algorithms ◽  
Geometric Optimization ◽  
Trial And Error ◽  
Design Problems ◽  
Design Iteration ◽  
Optimization Methodology ◽  
Reflecting Surfaces ◽  
Specular Surfaces

This paper presents an optimization methodology for designing radiant enclosures containing specularly-reflecting surfaces. The optimization process works by making intelligent perturbations to the enclosure geometry at each design iteration using specialized numerical algorithms. This procedure requires far less time than the forward “trial-and-error” design methodology, and the final solution is near optimal. The radiant enclosure is analyzed using a Monte Carlo technique based on exchange factors, and the design is optimized using the Kiefer-Wolfowitz method. The optimization design methodology is demonstrated by solving two industrially-relevant design problems involving two-dimensional enclosures that contain specular surfaces.

scholarly journals SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 250 ◽  
Author(s):  
Umesh Balande ◽  
Deepti Shrimankar
Keyword(s):  
Constrained Optimization ◽  
Engineering Design ◽  
Real World ◽  
Optimization Design ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Design Problems ◽  
Improved Firefly Algorithm ◽  
Engineering Design Problems ◽  
And Performance

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

scholarly journals Effective Hybrid Algorithm of Taguchi Method, FEM, RSM, and Teaching Learning-Based Optimization for Multiobjective Optimization Design of a Compliant Rotary Positioning Stage for Nanoindentation Tester

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Minh Phung Dang ◽  
Thanh-Phong Dao ◽  
Ngoc Le Chau ◽  
Hieu Giang Le
Keyword(s):  
Multiobjective Optimization ◽  
Taguchi Method ◽  
Safety Factor ◽  
Optimization Design ◽  
Hybrid Approach ◽  
Numerical Data ◽  
Hybrid Optimization Algorithm ◽  
Positioning Stage ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper proposes an effective hybrid optimization algorithm for multiobjective optimization design of a compliant rotary positioning stage for indentation tester. The stage is created with respect to the Beetle’s profile. To meet practical demands of the stage, the geometric parameters are optimized so as to find the best performances. In the present work, the Taguchi method is employed to lay out the number of numerical experiments. Subsequently, the finite element method is built to retrieve the numerical data. The mathematical models are then established based on the response surface method. Before conducting the optimization implementation, the weight factor of each response is calculated exactly. Based on the well-established models, the multiple performances are simultaneously optimized utilizing the teaching learning-based optimization. The results found that the weight factors of safety factor and displacement are 0.5995 (59.95%) and 0.4005 (40.05%), respectively. The results revealed that the optimal safety factor is about 1.558 and the optimal displacement is 2.096 mm. The validations are in good agreement with the predicted results. Sensitivity analysis is carried out to identify the effects of variables on the responses. Using the Wilcoxon’s rank signed test and Friedman test, the effectiveness of the proposed hybrid approach is better than that of other evolutionary algorithms. It ensures a good effectiveness to solve a complex multiobjective optimization problem.

A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms

2021 ◽  
Vol 12 (2) ◽  
pp. 21-35
Author(s):  
Archana Patnaik ◽  
Neelamdhab Padhy
Keyword(s):  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Design Problems ◽  
Detection Algorithms ◽  
Average Accuracy ◽  
Code Smell ◽  
Open Source Systems ◽  
Efficient Recognition

Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.

Application of Intelligent Optimization Algorithm in Mechanical Design

2015 ◽  
Vol 713-715 ◽  
pp. 2049-2052
Author(s):  
Sha Sha Dou
Keyword(s):  
Design Theory ◽  
Optimization Design ◽  
Mechanical Design ◽  
Design Method ◽  
Computational Effort ◽  
Ant Algorithm ◽  
Design Problems ◽  
Intelligent Optimization Algorithm ◽  
Design Requirements ◽  
Mechanical Optimization

Mechanical optimization design is a new design method in the development foundation of the modern mechanical design theory, the application of optimization design in mechanical design can make the scheme achieve some optimization results in the design requirements specified, without consuming too much computational effort. The corresponding mathematical models of ant algorithm and Cellular ant algorithm are established, according to the actual mechanical design problems, and used to solve the established mathematical model by computer, so as to obtains the optimal design scheme.

Optimal Design of Automotive Steering Column Bearing by Simulated Annealing Algorithm

2011 ◽  
Vol 189-193 ◽  
pp. 1486-1493
Author(s):  
Sui Ran Yu ◽  
Jing Tao
Keyword(s):  
Simulated Annealing ◽  
Optimal Design ◽  
Optimization Design ◽  
Simulated Annealing Algorithm ◽  
Steering System ◽  
Steering Wheel ◽  
Optimized Design ◽  
Chinese Market ◽  
Design Problems ◽  
Annealing Algorithm

The steering column assembly which connects the steering wheel and the steering gear is a very important part of the steering system. This paper presented the optimization design and testing of the steering column upper bearing of an upcoming automobile targeting the 2010-2015 Chinese market. The design problems were formulated into an optimization problem and were solved by Simulated Annealing Algorithm. 6 sample bearings were produced according to the optimization results and tests of steering upper bearing durability, steering shaft looseness and rotation effects performed on each sample to verify the optimized design. According to the test results, the optimal design of the steering column upper bearing is reliable. By using the Simulated Annealing Algorithm, the design time and costs of the steering upper bearing were reduce at least by 2 months and 60,000 USD, respectively.

scholarly journals A Hybrid Recommender System for HCI Design Pattern Recommendations

2021 ◽  
Vol 11 (22) ◽  
pp. 10776
Author(s):  
Amani Braham ◽  
Maha Khemaja ◽  
Félix Buendía ◽  
Faiez Gargouri
Keyword(s):  
Recommender System ◽  
Interface Design ◽  
Design Patterns ◽  
Hybrid Approach ◽  
Evaluation Study ◽  
Design Problems ◽  
Post Test ◽  
Standard Solution ◽  
Hybrid Recommender ◽  
Selection Of

User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively.

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