tabu
1.Optimization of Sectional Area of Core of Transformer Based on Tabu Algorithm
2.Here the tabu search is used for the solution space in the process of mutation. The hybrid algorithm is devised to solve the portfolio investment model in terms of probability criterion.
3.Methods for optimization design of discrete variables are summarized. The tabu search algorithm and its application to the optimization design of discrete variables are presented and explained by an example.
4.Based on the result, a novel wideband code division multiple (WCDMA) cell planning algorithm was proposed, which uses the set covering for overall planning and applies the tabu search for further local optimization.
5.With the development of the stochastic global optimization method such as tabu search, simulated annealing and evolutionary computation during 80 age in the 20th century, some authors study the theory and application of those algorithms and put forward novel algorithms and solve a lot of practical problems.
6.It designs the organization mode of the DFC in term of supply chain, studies the methods of the DFC, and presents the cost optimization methods based on neural network, Genetic Algorithm and tabu search.
7.After reviewing and analysis of the present state and development in the belt conveyor electrical drive system, the PID controller based on the improved genetic tabu algorithms is proposed as speed regulator to improve the performance of DTC system during parameter variations and load disturbance in belt conveyor drive system.
8.Based on tabu search algorithms and Matlab, optimization simulation has been done. Compared with original design, the simulation results showed that plentiful coefficient increased by 1 24%, dynamical maximum positive acceleration lowered by 0 87% in ascending and increased by 5 23% in descending, the maximum negative acceleration decreased by 5 93%. The change of acceleration became more stable and the optimization effect was better than that of genetic algorithms.
9.The representative samples set is taken as the initial set of Tabu algorithm to further maintain. The method only considers the samples at different classes borders when samples are insert into new training set. The principles of deleting or inserting a sample are the higher categorization accuracy principle and the higher similarity with training set principle.
