5 research outputs found
Multiobjective differential evolution based on fuzzy performance feedback: Soft constraint handling and its application in antenna designs
The recently emerging Differential Evolution is considered one of the most powerful tools for solving optimization problems. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity, robustness and high speed of convergence. Differential evolution is attractive to researchers all over the world as evidenced by recent publications. There are many variants of differential evolution proposed by researchers and differential evolution algorithms are continuously improved in its performance. Performance of differential evolution algorithms depend on the control parameters setting which are problem dependent and time-consuming task. This study proposed a Fuzzy-based Multiobjective Differential Evolution (FMDE) that exploits three performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. We apply the fuzzy inference rules to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in this algorithm. The proposed FMDE is evaluated on the well known ZDT, DTLZ, and WFG benchmark test suites. The experimental results show that FMDE is competitive with respect to the chosen state-of-the-art multiobjective evolutionary algorithms. The advanced version of FMDE with adaptive crossover rate (AFMDE) is proposed. The proof of concept AFMDE is then applied specifically to the designs of microstrip antenna array. Furthermore, the soft constraint handling technique incorporates with AFMDE is proposed. Soft constraint AFMDE is evaluated on the benchmark constrained problems. AFMDE with soft constraint handling technique is applied to the constrained non-uniform circular antenna array design problem as a case study
āļāļēāļĢāļāļąāļāļāļēāđāļāļāļāļģāļĨāļāļāđāļāđāļāļāđāđāļāļĨāļīāđāļĄāļāļāđāļŠāļēāļŦāļĢāļąāļāļāļēāļĢāļāļģāđāļāļīāļāļāļĨāļ·āđāļāđāļĄāļāļāļĩāđāļāļāļ°āļāļđāļŠāļāļīāļāđāļāļ·āđāļāļāļēāļĢāļŠāļĢāđāļēāļāļ āļēāļāļāļąāļāļāļ§āļēāļāđāļāļāđāļĄāđāļāļģāļĨāļēāļĒ
āļĢāļēāļĒāļāļēāļāļ§āļīāļāļąāļĒ -- āļĄāļŦāļēāļ§āļīāļāļĒāļēāļĨāļąāļĒāđāļāļāđāļāđāļĨāļĒāļĩāļĢāļēāļāļĄāļāļāļĨāļāļĢāļ°āļāļāļĢ, 2561This research report describes the study of the development of finite element modeling for magnetoacoustic wave generation for non-destructive tomography. In the study, the two-dimensional finite element model is used for the generation of magnetoacoustic wave in the mimic biological tissue model. The Gaussian pulse is applied as the excitation pulse in this research. The result of signal capturing at the boundary of mimic tissue model is used for the reconstruction cross-section image which is agreed to the original conductivity distribution.Rajamangala University of Technology Phra Nakho
āđāļāļāļāļģāļĨāļāļāļāļāļāđāļāļāļāđāļāļēāļĢāļŦāļĒāļļāļāļāļģāļāļēāļāļāļāļāļāļąāļāļāļĩāđāļāļīāļāđāļāļāđāļĢāļāđāļāļĩāļĒāļĨāļāļīāđāļ§āļĨāļĨāļđāļāļąāļ āđāļāļāļŦāļĨāļēāļĒāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđ āđāļāļĒāļāļēāļĢāļāđāļāļāļāļĨāļąāļāđāļĄāļāļĢāļīāļāļŠāļĄāļĢāļĢāļāļāļ°
āļĢāļēāļĒāļāļēāļāļ§āļīāļāļąāļĒ -- āļĄāļŦāļēāļ§āļīāļāļĒāļēāļĨāļąāļĒāđāļāļāđāļāđāļĨāļĒāļĩāļĢāļēāļāļĄāļāļāļĨāļāļĢāļ°āļāļāļĢ, 2562Differential evolution is one of the most efficient optimization algorithms for solving complication problems including single objective, multiobjective and many-objective optimization. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity, robustness and high speed of convergence. The Advanced Fuzzy-based Multiobjective Differential Evolution (AFMDE) that exploits three performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. The fuzzy inference rules are applied to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in AFMDE. The optimization algorithm will stop the evolution process if the number of iterations reaches the stopping criteria which usually is the maximum number of iterations. Then, the optimization algorithm delivers the optimal solution founded. However, sometimes if the maximum number of iterations is not appropriately defined, the found solutions may not be the optimal ones. In case of the optimization algorithm has found the optimal solutions but it must continue the evolution process because the stopping criteria are not met. This can cause unnecessary using of high computational resources and time-consuming. Therefore, this research study proposed the stopping criteria based on performance metrics feedback for AFMDE. The efficiency of the proposed criteria combined with AFMDE is evaluated on the well-known ZDT benchmark test suites.Rajamangala University of Technology Phra Nakho
āļāļēāļĢāļĻāļķāļāļĐāļēāļ§āļīāļāļąāļĒāļāļĨāļēāļāđāļĢāļāļāļēāļ (Target Market) āļāļāļāļāļāļ°āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļāļĢāđ āļāļąāļāļāļēāļĢāļāđāļēāļ§āđāļāđāļēāļŠāļđāđāļāļļāļāļŠāļēāļŦāļāļĢāļĢāļĄ 4.0 āđāļāļ·āđāļāļāļ§āļēāļĄāļĒāļąāđāļāļĒāļ·āļ
āļĢāļēāļĒāļāļēāļāļ§āļīāļāļąāļĒ -- āļĄāļŦāļēāļ§āļīāļāļĒāļēāļĨāļąāļĒāđāļāļāđāļāđāļĨāļĒāļĩāļĢāļēāļāļĄāļāļāļĨāļāļĢāļ°āļāļāļĢ, 2559Since the industries are developing to Industry 4.0 in the near future. Therefore, a study of faculty of engineering, Rajamangala University of Technology Phra Nakhon, target market with Industry 4.0 for sustainability is the necessity. In order to collect the data, build the knowledge about the target market in the Industry 4.0 era. Moreover, a survey of the requirement of graduatesâ characteristic that can support the revolution from Industry 3.0 to Industry 4.0 is very important. Therefore, the faculty of engineering, Rajamangala University of Technology Phra Nakhon can well prepare the graduates production for supporting the Industry 4.0 for sustainability.Rajamangala University of Technology Phra Nakho
āļāļēāļĢāļĻāļķāļāļĐāļēāđāļāļ§āļāļģāđāļāļīāļāļāļēāļĢāļŦāļĨāļąāļāđāļāļāļēāļĢāļāļąāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļāļāļāļāļ°āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļāļĢāđ āļāļĩāđāļāļđāļĢāļāļēāļāļēāļĢāļāļēāļĢāđāļĢāļĩāļĒāļāļĢāļđāđāļāļąāļāļāļēāļĢāļāļģāļāļēāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāļāļĨāļīāļāļāļąāļāļāļīāļāļĄāļ·āļāļāļēāļāļĩāļ
āļĢāļēāļĒāļāļēāļāļ§āļīāļāļąāļĒ -- āļĄāļŦāļēāļ§āļīāļāļĒāļēāļĨāļąāļĒāđāļāļāđāļāđāļĨāļĒāļĩāļĢāļēāļāļĄāļāļāļĨāļāļĢāļ°āļāļāļĢ, 2559Nowadays, it is rapid technology advancement. The faculty of engineering, Rajamangala University of Technology Phra Nakhon, needs to update the bachelor degree programs and produces graduates that meet the requirements of the target markets. Work integrated learning education is one of the most effective education that is suitable for this situation and the future. This research is a study of faculty of engineeringâs strategy base on work integrated learning for professional graduate production. The research results acknowledge the work integrated learning education type that is implemented in the faculty of engineering, Rajamangala University of Technology Phra Nakhon. In addition, the results can be applied to the modification of the engineering curriculum or the new engineering curriculum design.Rajamangala University of Technology Phra Nakho