micromulti-objective evolutionary algorithms (mu MOEAs) are designed to address multi-objective optimization problems (MOPs), particularly in low-power microprocessor where computing resources are constrained. Howeve...
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micromulti-objective evolutionary algorithms (mu MOEAs) are designed to address multi-objective optimization problems (MOPs), particularly in low-power microprocessor where computing resources are constrained. However, to compensate for the diversity loss resulting from using a micro population, existing optimization methods in numerous mu MOEAs lead to diminished competitiveness over time due to the absence of targeted feedback on population states, hindering further performance improvement. To address this challenge, a micro multi-objective genetic algorithm with information fitting strategy for low-power microprocessor(mu MOGAIF) is proposed, which utilizes an information fitting strategy to monitor the evolutionary status of the population and to facilitate method selection. The status information is collected at each iteration and fitted regularly, and the evaluation indicator is adjusted by the fitted evaluation results. In addition, adaptive mating selection is used in the construction of the mating pool to enhance the exploitation of solutions in probable regions. To enhance the adaptability of mu MOGAIF, dual archives are established, one archive compensates the output using various strategies to pursue convergence or diversity, while the other provides the final output set. mu MOGAIF is compared with five state-of-the-art MOEAs and five mu MOEAs on the DTLZ, WFG, MaF, and ZDT benchmark test suites, and the experimental results demonstrate that mu MOGAIF has outstanding performance. Furthermore, simulations based on low-power microprocessor have been conducted to verify the feasibility of mu MOGAIF.
In this paper, a novel multi-objective optimization method is suggested based on an approximation model management technique. It is a sequential approximation method, in which a multi-objective optimization with appro...
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In this paper, a novel multi-objective optimization method is suggested based on an approximation model management technique. It is a sequential approximation method, in which a multi-objective optimization with approximation models subject to design variable move limits is iterated until convergence. In each iteration step, the approximation models are constructed by the response surface approximations with the samples which are obtained from the design of experiments, and a Pareto optimal set predicted by the approximations is identified through a multi-objectivegeneticalgorithm. According to the prediction of the approximation models, a move limits updating strategy is employed to determine the design variable move limits for the next iteration. At the end of each iteration step, some uniform distributed points chosen from the predictive Pareto optimal frontier are verified by the high fidelity models and the obtained actual Pareto optimal set is stored in an external archive. The high efficiency of the present method is demonstrated by four different test functions and two engineering applications. (C) 2008 Elsevier B.V. All rights reserved.
A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing de...
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A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction,the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system. (C) 2016 Society of CAD/CAM Engineers. Publishing Services by Elsevier.
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