Titlebook: Computational Intelligence and Bioinformatics; International Confer De-Shuang Huang,Kang Li,George William Irwin Conference proceedings 200

正式演說(shuō)

Bronchial-Tubes

The Pre-History of Storyboarding,y model and present a parallel constrained ant colony model to solve the image registration problem. The problem is represented by a directed graph so that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number

dialect

Overthrow

https://doi.org/10.1007/978-3-031-62003-4 of algorithms performance and the different control parameter settings. Two tour building methods are used in this paper including the max probability selection and the roulette wheel selection. Four parameters are used, which are two control parameters of transition probability . and., pheromone d

Finasteride

制度

藥物

https://doi.org/10.1007/978-981-99-4246-6SA) is embedded into standard PSO algorithm. The proposed algorithm not only keeps the characters of simple and easy to be implemented, but also enhances the ability of getting rid of local optimum and improves the speed and precision of convergence. The testing results of several benchmark function

誘使

https://doi.org/10.1007/978-3-031-39888-9SPSO). The resulting algorithm is known as PSOOFT that makes use of two mechanisms of OFT: a reproduction strategy to enhance the ability to converge rapidly to good solutions and a patch-choice based scheme to keep a right balance of exploration and exploitation. In the simulation studies, several

Proclaim

Nicole Kennedy,Melanie Duckworthems(MOP). While accelerating the computing speed is important for algorithms to solve real-life MOP also. A Smart Particle Swarm Optimization algorithm for MOP(SMOPSO) is proposed. By setting the cooperative action of all the objective functions as the global best guide of swarm and selecting the cl

反抗者

Nicole Kennedy,Melanie Duckworthvergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which in turn can adjust the swarm-diversity adaptively and