Earticle

Optimization is ability of find the best solution in the existing situations. Optimization is used in design and maintenance of systems engineering, economic, social and even necessary to reduce costs and increase profits. The widespread importance of optimization problem has a lot of grown. There are many algorithms for optimization and they are trying to reduce the disadvantages of other methods and increase the ability of resolve the problem. This paper proposed an adaptive ABC and Bat algorithm. The idea of algorithm is improved speed of convergence and optimized search in search space for ABC algorithm with Bat algorithm. The proposed algorithm is compared with ABC and Bat algorithm on benchmark function and test shows ABC_M are improved obviously. Also can be known a complete local search is more important from global search.

Cloud storage systems has played an important role in the support of large-scale and high-performance cloud application. For the purposes of the cloud storage system, data migration is the key technology to the elastic load balancing.In this paper, we take data migration issues into the load-balancing scenarios and propose the data migration method based on the ABC algorithm. At the same time, we validated the method.In the method,we compute the load of each storage node through the comprehensive evaluation system.The system contains the following four aspects, including the available CPU,available memory, data access heat and the system response time.The cloud storage system performs the data migration operation according to the data obtained from the comprehensive evaluation system.The results show that this method can satisfy the application needs.At the same time, this method can reflect the integrated load of each node, and it also can achieve the optimal performance of the cloud storage system.

Group mobile robot path planning is a multi-objective optimization problem, as the requirement of obstacle avoidance, traditional robot path planning optimization method has the problem of algorithm complexity, large search space and low efficiency, it is difficult to obtain the optimal solution. In order to improve the efficiency and the positioning accuracy of group robot path planning, we put forward a group mobile robot path planning method based on variable dimension artificial bee colony algorithm. Firstly, we take robot working environment to model, then taking group robot path network as nectar source, the ultimate goal of path planning is to find minimal path network, to find an optimal swarm robot moving path network which avoids obstacles through the mutual cooperation between bees. Simulation experiment results show that the path planning method based on variable dimension artificial bee colony algorithm improves the efficiency of swarm robot path planning, it can find optimal solution of swarm robot path planning during the shortest time, and it can avoid obstacles safely, it provides basis to group robot task coordination.

Considering that the existing artificial bee colony (ABC) algorithm can not give simultaneously attention to evolution speed and solution quality, an improved ABC algorithm is proposed based on nelder-mead simplex method (NMSM) in this paper, and defined ad NMSM-ABC. In the process of iteration, the algorithm periodically passes the best individual vertex from NMSM operator into ABC, or migrates the optimal food source information from ABC to NMSM can get away from local minimum with aid of ABC. Hence, the proposed algorithm can realize cooperative co-evolution of the two so that the desired properties are obtained. In addition, the sensitivity analysis of the key parameter of NMSM-ABC is also conducted and the best value is suggested. Finally, Numerical experiments and comparisons on 6 benchmark functions are conducted with other ABC algorithms, and the results indicate that the proposed algorithm effectively overcomes the local minimum, and dramatically enhances the global searching ability and convergence speed, and is a good cooperative co-evolution algorithm.

중앙 집중적인 단일 컨트롤러를 이용할 경우 메시지 과부하로 인해 응답이 지연될 수 있으므로 스위치들이 기존의 컨트롤러를 대신하여 새로운 컨트롤러와 연결되어 트래픽을 처리하는 다중 컨트롤러가 효율적이다. 본 논문에서는 SDN 분산 컨트롤러에서 일관성 문제를 해결하기 위해 우선순위에 기반을 둔 향상된 인공벌 군집(ABC) 알고리즘을 제안한다.