This paper presents a combination method of Particle Swarm Optimization (PSO) and topology optimization. With this method a better result can be achieved compared with the sequential application of these two optimizat...This paper presents a combination method of Particle Swarm Optimization (PSO) and topology optimization. With this method a better result can be achieved compared with the sequential application of these two optimization methods. It inherits the ability in finding global optimum from PSO and also suits for discretized design domain. Some special schemes are used in order to provide higher computation efficiency. This method has only been tested with a convex optimization problem. The application in case of a concave problem will be a future study.展开更多
Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional ...Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional collaborative filtering algorithms, this paper is going to come up with improvements and construct a hybrid collaborative filtering algorithm model which will possess excellent scalability. Meanwhile, this paper will also optimize the process based on the parameter selection of genetic algorithm and demonstrate its pseudocode reference so as to provide new ideas and methods for the study of parameter combination optimization in hybrid collaborative filtering algorithm.展开更多
文摘This paper presents a combination method of Particle Swarm Optimization (PSO) and topology optimization. With this method a better result can be achieved compared with the sequential application of these two optimization methods. It inherits the ability in finding global optimum from PSO and also suits for discretized design domain. Some special schemes are used in order to provide higher computation efficiency. This method has only been tested with a convex optimization problem. The application in case of a concave problem will be a future study.
文摘Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional collaborative filtering algorithms, this paper is going to come up with improvements and construct a hybrid collaborative filtering algorithm model which will possess excellent scalability. Meanwhile, this paper will also optimize the process based on the parameter selection of genetic algorithm and demonstrate its pseudocode reference so as to provide new ideas and methods for the study of parameter combination optimization in hybrid collaborative filtering algorithm.
