This paper proposed an enhanced NEH with full insertion moves to solve the permutation flow shop problem.The characteristics of the original NEH are investigated and analyzed,and it is concluded that the given method ...This paper proposed an enhanced NEH with full insertion moves to solve the permutation flow shop problem.The characteristics of the original NEH are investigated and analyzed,and it is concluded that the given method would be promising to find better solutions,while the cost would be increased.Fast makespan calculating method and eliminating non-promising permutation policy are introduced to reduce the evaluation effort.The former decreases the time complexity from O(n4m) to O(n3m),which is an acceptable cost for medium and small size instances considering the obtained solution quality.The results from computational experience show that the latter also can eliminate a lot of non-promising solutions.展开更多
In this study, hybrid computational frameworks are developed for active noise control（ANC） systems using an evolutionary computing technique based on genetic algorithms（GAs） and interior-point method（IPM）, follo...In this study, hybrid computational frameworks are developed for active noise control（ANC） systems using an evolutionary computing technique based on genetic algorithms（GAs） and interior-point method（IPM）, following an integrated approach, GA-IPM. Standard ANC systems are usually implemented with the filtered extended least mean square algorithm for optimization of coefficients for the linear finite-impulse response filter, but are likely to become trapped in local minima（LM）. This issue is addressed with the proposed GA-IPM computing approach which is considerably less prone to the LM problem. Also, there is no requirement to identify a secondary path for the ANC system used in the scheme. The design method is evaluated using an ANC model of a headset with sinusoidal, random, and complex random noise interferences under several scenarios based on linear and nonlinear primary and secondary paths. The accuracy and convergence of the proposed scheme are validated based on the results of statistical analysis of a large number of independent runs of the algorithm.展开更多
基金New Century Excellent Talents in University (No.NCET04-0383)Science and Technology Phosphor Program of Shanghai (No.04QMH1405)
文摘This paper proposed an enhanced NEH with full insertion moves to solve the permutation flow shop problem.The characteristics of the original NEH are investigated and analyzed,and it is concluded that the given method would be promising to find better solutions,while the cost would be increased.Fast makespan calculating method and eliminating non-promising permutation policy are introduced to reduce the evaluation effort.The former decreases the time complexity from O(n4m) to O(n3m),which is an acceptable cost for medium and small size instances considering the obtained solution quality.The results from computational experience show that the latter also can eliminate a lot of non-promising solutions.
文摘In this study, hybrid computational frameworks are developed for active noise control（ANC） systems using an evolutionary computing technique based on genetic algorithms（GAs） and interior-point method（IPM）, following an integrated approach, GA-IPM. Standard ANC systems are usually implemented with the filtered extended least mean square algorithm for optimization of coefficients for the linear finite-impulse response filter, but are likely to become trapped in local minima（LM）. This issue is addressed with the proposed GA-IPM computing approach which is considerably less prone to the LM problem. Also, there is no requirement to identify a secondary path for the ANC system used in the scheme. The design method is evaluated using an ANC model of a headset with sinusoidal, random, and complex random noise interferences under several scenarios based on linear and nonlinear primary and secondary paths. The accuracy and convergence of the proposed scheme are validated based on the results of statistical analysis of a large number of independent runs of the algorithm.
