Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using ...
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Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreov
The superior solution set search problem contains parameters that provide constraints on evaluation value and distance. Therefore, in this article, we propose an evaluation indicator that is inspired by a method based...
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The superior solution set search problem contains parameters that provide constraints on evaluation value and distance. Therefore, in this article, we propose an evaluation indicator that is inspired by a method based on a dominance relation in multiobjectiveoptimization problems and includes the aforementioned parameters. We also propose a search method based on the genetic algorithm (GA) with this indicator and perform numerical experiments on unique superior solution set search problems. The proposed method finds more superior solutions than the conventional single-objective optimization method, which confirms its usefulness. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Nowadays, usage of optimization methods and techniques in various applications is the key factor of increasing the systems efficiency and performance. In this paper, Proportional-Integral-Derivative (PID) controller o...
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ISBN:
(纸本)9781479929948
Nowadays, usage of optimization methods and techniques in various applications is the key factor of increasing the systems efficiency and performance. In this paper, Proportional-Integral-Derivative (PID) controller optimization in greenhouse lighting control system is studied by tuning PID controller coefficients. The advantages and disadvantages of employing multi-objectiveoptimization methods have been discussed in many cases by researchers and engineers. In this regard, we consider the optimizing problem with varying number of objectives which are 1, 2 and 3 objectives. The simulation results verify that the improvement of the system output responses would be achieved by increasing the number of separated and independent objective functions.
Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are...
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ISBN:
(纸本)9781479944781
Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objectiveoptimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.
We take Germany as an example to derive a dynamic model which describes the relation between supply and demandThrough the data we apply least square method to fit the demand changing over time, as well as single objec...
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We take Germany as an example to derive a dynamic model which describes the relation between supply and demandThrough the data we apply least square method to fit the demand changing over time, as well as singleobjectiveoptimization and radiation network to acquire the mechanism of distribution and transportation of the resources to refugees.
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