Huang Han

Runtime analysis for continuous (1+1) evolutionary algorithm based on average gain model

Runtime analysis of continuous evolutionary algorithm (EA) is an open problem in theoretical foundation of evolutionary computation. There are fewer results about it than the runtime studies of discrete EA. For an example of (1+1)EA, an average gain model and its calculating method were proposed to produce a theory of runtime analysis as an index of computational time complexity. The average gain was computed to estimate the average runtime of two (1+1)EAs based on the mutation of standard normal distribution and uniform distribution, for Sphere function which is focused on by many researchers. The analysis result indicates that computational time complexity of the (1+1)EAs is exponential order. Furthermore, the solution speed of uniform-distribution mutation is faster than standard normal distribution with the same error accuracy and initial distance. Numerical results also verify the correctness of the proposed theory and the usefulness of the average gain model.

Using Particle Swarm Large-scale Optimization to Improve Sampling-based Image Matting

Sampling-based image matting is an important basic operator of image processing. The matting results are depended on the quality of sample selection. The sample selection produces a pair of samples for each pixel to detect whether the pixel is in the foreground of an image. Therefore, how to optimize the production is usually modeled as a large-scale optimization problem. In this study, particle swarm optimization is applied to solve the problem because its property of rapid convergence is positive to the real-time demand of image matting. We regard every two dimensions of a particle as a sample pair for a undetermined pixel. The encoding can make image matting more effective when there are relevant pixels in the image. The experimental result indicates that the proposed particle swarm optimization performs better than existing optimization method for image matting.

Superior-in-Status Analysis of Improved Genetic Algorithm for GTSP

Superiority in status relation (≻) can be used to rank the given EAs in terms of convergence capacity. The performance of an EA can be improved if it is modified to be superior in status to its original version. In this paper, the (≻) relation model is applied to analyzing the improvement of generalize-chromosome genetic algorithm (GCGA) for generalized traveling salesman problem (GTSP). Hybrid-chromosome genetic algorithm (HCGA) is superiority to GCGA. The numerical results also indicate that HCGA performs better and more steadied than GCGA in solving several GTSP instances. The case is the application example of the proposed relation model.

QoSR Algorithm of Q-ACO Based on Convergence Grads Expectation

Although we can get the optimal path of network by ACO, there are too many interative times and the convergence speed is too slow. This paper proposes the Q-ACO QoSR based on convergence expectation with the real-time and the high efficiency of network. The algorithm defines index expectation function of link and proposes convergence expectation and convergence grads. This method improves the ability of routing and convergence speed. It can get the optimal path at a high efficiency by comparing the convergence grads quickly.Although we can get the optimal path of network by ACO, there are too many interative times and the convergence speed is too slow. This paper proposes the Q-ACO QoSR based on convergence expectation with the real-time and the high efficiency of network. The algorithm defines index expectation function of link and proposes convergence expectation and convergence grads. This method improves the ability of routing and convergence speed. It can get the optimal path at a high efficiency by comparing the convergence grads quickly.

A Memetic Algorithm Applied to Allocation Problem of the Concrete Mixing Plants

This paper addresses the allocation problem of the concrete mixing plants (APCMP). We present a memetic algorithm with the combination of ant colony optimization and greedy algorithm to solve the problem. Ant colony optimization is used to achieve the global search, and greedy algorithm based on the shortest distance of the sites is introduced to proceed the local search. It can be obtained the optimization solution to guarantee minimum the total transport distances. In the end, through the experiment, the results show that memetic algorithm is better to solve the APCMP problem than the single greedy algorithm based on the shortest distance.