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So an appropriate window width h should be chosen considering the requirements for the accuracy and calculating speed of algorithms.
We present larger stepsizes in (31) and (59) than those in Algorithms 2.1 and 2.2 in [15], which leads to a better contraction property and faster convergence speed of Algorithms 3.1 and 3.2.
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The main idea is based on the efficient use of a genetic algorithm's crossover operator to enhance the speed of algorithm to reach an optimal solution.
In addition, inspired by domain knowledge, the initial solution generation method and feasible space identification method are designed to promote the convergence speed of algorithm.
The experiments results show that our approach can enhance convergence speed of algorithm and solve the problems that particle swarm algorithm easily falls into the local optimal solution and has low search accuracy.
So, the speed of algorithm is low.
Thus, it can enhance the convergence speed of algorithm, improve efficiency for solution, and avoid the local optimal and precocious defects [32].
In practice, the adaptive stepsize scheme in [24] can be used to control the convergence speed of Algorithm 1 and limit the runtime of Algorithm 1. Figure 4 Evolution of the probabilities of auction mechanisms (q) for the primary user.
Comparing Table 1 with Table 2, we can intuitively see that the convergence speed of Algorithm 3.1 for (rho_{k}=0) is faster than that of Algorithm 1.1 constructed in [9].
Also, the parameters, which are required for calculating balloon energy and the number of iteration in each frame is obtained automatically, so that the speed of algorithm is improved.
The various simulations presented in this paper and the comparisons made with real-world data proved that the proposed modifications can highly improve the accuracy and speed of algorithm.
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