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Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

The vessel collision accidents cause a great loss of lives and property. In order to reduce the human fault and greatly improve the safety of marine traffic, collision avoidance strategy optimization is proposed to achieve this. In the paper, a multiobjective optimization algorithm NSGA-II is adopted to search for the optimal collision avoidance strategy considering the safety as well as economy elements of collision avoidance. Ship domain and Arena are used to evaluate the collision risk in the simulation. Based on the optimization, an optimal rudder angle is recommended to navigator for collision avoidance. In the simulation example, a crossing encounter situation is simulated, and the NSGA-II searches for the optimal collision avoidance operation under the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). The simulation studies exhibit the validity of the method.

This paper is taking actual control demand of rotary kiln as background and builds a calcining belt state space model using PO-Moesp subspace method. A novel order-delay double parameters error criterion (ODC) is presented to reduce the modeling order. The proposed subspace order identification method takes into account the influence of order and delay on model error criterion simultaneously. For the introduction of the delay factors, the order is reduced dramatically in the system modeling. Also, in the data processing part sliding-window method is adopted for stripping delay factor from historical data. For this, the parameters can be changed flexibly. Some practical problems in industrial kiln process modeling are also solved. Finally, it is applied to an industrial kiln case.

PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However,Kp,Ki, andKdof PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters.

Intracellular precursor supply is a critical factor for amino acid productivity. In the present study,ppsAandtktAgenes were overexpressed in genetically engineeredEscherichia colito enhance the availability of two precursor substrates, phosphoenolpyruvate and erythrose-4-phosphate. The engineered strain, TRTH0709 carrying pSV709, produced 35.9 g/L tryptophan from glucose after 40 h in fed-batch cultivation. The two genes were inserted, independently or together, into a low-copy-number expression vector (pSTV28) and transferred to TRTH0709. Fed-batch fermentations at high cell densities of the recombination strains revealed that overexpression of theppsAgene alone does not significantly increase tryptophan yield. On the other hand, overexpression of thetktAgene, alone or with theppsAgene, could further improve tryptophan yield to a final tryptophan titer of 37.9 and 40.2 g/L, respectively. These results represent a 5.6% and 11.9% enhancement over the titer achieved by TRTH0709. No evident genetic modifications leading to growth impairment were observed.