Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

Designing Practical Teaching System for Outside-school Practice Base

Abstract. A great deal of department in enterprise how to optimize practicing-teaching contents, and forms the practicing-teaching system, in building Beijing level Talent training base of outsideschool. The system consist of three parts which is the cognitive course content design, the hardware practice course content design and the software test theoretical teaching content design. We improved concepts, formed a detail enterprise practice curriculum program, and do it in practice. This practice teaching system is a featured program of our university. It has been carried out for 5 years, improving students' engineering and practice skills and therefore fostering eligible persons with various abilities and qualities for the development and prosperity of our country

Differential evolution with two-level parameter adaptation

The performance of differential evolution (DE) largely depends on its mutation strategy and control parameters. In this paper, we propose an adaptive DE (ADE) algorithm with a new mutation strategy DE/lbest/1 and a two-level adaptive parameter control scheme. The DE/lbest/1 strategy is a variant of the greedy DE/best/1 strategy. However, the population is mutated under the guide of multiple locally best individuals in DE/lbest/1 instead of one globally best individual in DE/best/1. This strategy is beneficial to the balance between fast convergence and population diversity. The two-level adaptive parameter control scheme is implemented mainly in two steps. In the first step, the population-level parameters F p and CR p for the whole population are adaptively controlled according to the optimization states, namely, the exploration state and the exploitation state in each generation. These optimization states are estimated by measuring the population distribution. Then, the individual-level parameters F i and CR i for each individual are generated by adjusting the population-level parameters. The adjustment is based on considering the individual's fitness value and its distance from the globally best individual. This way, the parameters can be adapted to not only the overall state of the population but also the characteristics of different individuals. The performance of the proposed ADE is evaluated on a suite of benchmark functions. Experimental results show that ADE generally outperforms four state-of-the-art DE variants on different kinds of optimization problems. The effects of ADE components, parameter properties of ADE, search behavior of ADE, and parameter sensitivity of ADE are also studied. Finally, we investigate the capability of ADE for solving three real-world optimization problems

Designing Practical Teaching System for Outside-school Practice Base

Abstract. A great deal of department in enterprise how to optimize practicing-teaching contents, and forms the practicing-teaching system, in building Beijing level Talent training base of outsideschool. The system consist of three parts which is the cognitive course content design, the hardware practice course content design and the software test theoretical teaching content design. We improved concepts, formed a detail enterprise practice curriculum program, and do it in practice. This practice teaching system is a featured program of our university. It has been carried out for 5 years, improving students' engineering and practice skills and therefore fostering eligible persons with various abilities and qualities for the development and prosperity of our country