By Sirapat Boonkrong, Herwig Unger, Phayung Meesad (eds.)
Computer and knowledge know-how (CIT) are actually all for governmental, business, and company domain names greater than ever. hence, it's important for CIT team of workers to proceed educational learn to enhance know-how and its adoption to fashionable purposes. The updated examine and applied sciences has to be allotted to researchers and CIT neighborhood always to help destiny improvement. The 10th foreign convention on Computing and knowledge expertise (IC 2 IT2014) prepared through King Mongkut's collage of expertise North Bangkok (KMUTNB) and companions presents an trade of the cutting-edge and destiny advancements within the key components of this method: computing device Networking and knowledge Mining. at the back of the history of the root of ASEAN, it turns into transparent that effective languages, enterprise ideas and conversation equipment must be tailored, unified and particularly optimized to realize a greatest gain to the clients and consumers of destiny IT systems.
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Extra resources for Recent Advances in Information and Communication Technology: Proceedings of the 10th International Conference on Computing and Information Technology (IC2IT2014)
Example text
PSOMN increase the global search capability and avoid premature convergence. However, selection of MaxStep is recommended within some range to obtain good results. 3 PSO with Dynamic Particles Re-initialization Period As previously mentioned, the main problem of applying particles re-initialization technique is parameters adjusting. To avoid this problem, the proposed algorithm automatically adjusts its parameters. In addition, the number of parameters is kept minimal. The trapping in local optimum is possible for standard PSO, which leads to stagnation of the searching in which the solution (GBEST) obtained at the point of trapping, is repeatedly produced irrespective of the length of searching time.
Its searching solution is poorer than setting re-initialization period to 100. Since swarm wastes time searching in local optimum rather than starting new search in other areas. For multimodal problems in Table 2, PSODR gets good results because of the reinitialization period setting ranged from 10 to 200. The PSODR algorithm can adjust re-initialization period in this range and it can obtain the good solutions as shown in Table 3. Therefore, the PSODR algorithm can dynamically adjust to suitably deal with the problem.
For example, suppose that = { , , … , } and a firefly = 1010001101, then an attribute subset is { , , , , }. 2 Fitness Function There are many definitions of fitness function for this problem in past literature [5][11]. There are some drawbacks with these fitness functions. These may considered by correctness of attribute reduction without minimal reductions [5]. The fitness function used in this research is defined as Eq. (14) [5] ( )= | | + | | (D) (14) where = | |, = | |, γ (D) is quality of classification.