• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1374-1381
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• 2022

Under the influence of nuclear radiation, the reliability of steam generators (SGs) is an important factor in the efficiency and safety of nuclear power plant (NPP) reactors. Motion planning that remotely manipulates an SG mobile tube-inspection robot to inspect SG heat transfer tubes is the mainstream trend of NPP robot development. To achieve motion planning, conditional traversal is usually used for base position optimization, and then the A^* algorithm is used for path planning. However, the proposed approach requires considerable processing time and has a single expansion during path planning and plan paths with many turns, which decreases the working speed of the robot. Therefore, to reduce the calculation time and improve the efficiency of motion planning, modifications such as the matrix method, improved parent node, turning cost, and improved expanded node were proposed in this study. We also present a comprehensive evaluation index to evaluate the performance of the improved algorithm. We validated the efficiency of the proposed method by planning on a tube sheet with square-type tube arrays and experimenting with Model SG.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.12-22
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• 2024

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

• Structural Engineering and Mechanics
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• v.90 no.3
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• pp.273-285
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• 2024

Tapered girders emerged as an economical remedy for the challenges associated with constructing long-span buildings. From an economic standpoint, these systems offer significant advantages, such as wide spans, quick assembly, and convenient access to utilities between the beam's shallow sections and the ceiling below. Elastic-local buckling is among the various failure modes that structural designers must account for during the design process. Despite decades of study, there remains a demand for efficient and comprehensive procedures to streamline product design. One of the most pressing requirements is a better understanding of the tapered web plate girder's local buckling behavior. This paper conducts a comprehensive numerical analysis to estimate the critical buckling coefficient for simply supported tapered steel web plates, considering loading conditions involving compression and bending stresses. An eigenvalue analysis was carried out to determine the natural frequencies and corresponding mode shapes of tapered web plates with varying geometric parameters. Additionally, the study highlights the relative significance of various parameters affecting the local buckling phenomenon, including the tapering ratio of the panel, normalized plate length, and ratio of minimum to maximum compressive stresses. The regression analysis and optimization techniques were performed using MATLAB software for the results of the finite element models to propose a separate formula for each load case and a unified formula covering different compression and bending cases of the elastic local buckling coefficient. The results indicate that the proposed formulas are applicable for estimating the critical buckling coefficient for simply supported tapered steel web plates.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.21-28
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• 2020

Fireworks Algorithm (FWA) is a relatively novel swarm-based metaheuristic algorithm for global optimization. To solve the low-efficient local searching problem and convergence of the FWA, this paper presents a Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator (namely VACUFWA). Firstly, the explosive amplitude is used to adjust improving the convergence speed dynamically. Secondly, Uniform Local Search (ULS) enhances exploitation capability of the FWA. Finally, the ULS and Variable Amplitude Coefficient operator are used in the VACUFWA. The comprehensive experiment carried out on 13 benchmark functions. Its results indicate that the performance of VACUFWA is significantly improved compared with the FWA, Differential Evolution, and Particle Swarm Optimization.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.3
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• pp.285-294
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• 2015

As a "kind of" mature ship form, planing hull has been widely used in military and civilian areas. Therefore, a reasonable design for planing hull becomes more and more important. For planing hull, resistance and trim are always the most important problems we are concerned with. It affects the planing hull's economic efficiency and maneuverability very seriously. Instead of the expensive towing tank experiments, the development of computer comprehensive ability allows us to previously apply computational fluid dynamics(CFD)to the ship design. In this paper, the CFD method and Goal Driven Optimization (GDO) were used in the estimations of planing hull resistance and running attitude to provide a possible method for performance computation of planing hull.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.653-665
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• 2010

In this study, we introduce a neurofuzzy approach to the design of fuzzy controllers. The development process exploits key technologies of Computational Intelligence (CI), namely, genetic algorithms (GA) and neurofuzzy networks. The crux of the design methodology deals with the selection and determination of optimal values of the scaling factors of fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out. Next, we form a nonlinear mapping for the scaling factors, which are realized by GA-based neurofuzzy networks by using a fuzzy set or fuzzy relation. The proposed approach is applied to control nonlinear systems like the inverted pendulum. Results of comprehensive numerical studies are presented through a detailed comparative analysis.

• Proceedings of the KOR-KST Conference
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• 2003.02a
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• pp.21-71
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• 2003

Previous alignment optimization models have not adequately considered intersections and other structures such as bridges, tunnels, grade separations and interchanges which can very strongly affect alignment decisions. This paper develops comprehensive cost functions for intersections and other structures and incorporates them in recently developed highway alignment optimization models connected with genetic algorithms and geographical information systems. The result is a fast and computerized process for extracting, analyzing spatial data, evaluating candidate alignments and optimizing them. A method for locally optimizing intersections is also developed. It improves search flexibility by saving good alignments whose unacceptable crossing angles with existing roads can be fixed, Through case studies, the developed model is found to produce feasible and efficient solutions.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.362-373
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• 2004

In this study, we introduce a noble neurogenetic approach to the design of the fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based NFN. The developed approach is applied to an inverted pendulum nonlinear system where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.