A Novel Approach for Library Materials Acquisition using Discrete Particle Swarm Optimization

The academic library materials acquisition problem is a challenge for librarian, since library cannot get enough funding from universities and the price of materials inflates greatly. In this paper, we analyze an integer mathematical model by considering the selection of acquired materials to maximize the average preference value as well as the budget execution rate under practical restrictions. The objective is to improve the Discrete Particle Swarm Optimization (DPSO) algorithm by adding a Simulate Annealing algorithm to reduce premature convergence. Furthermore, the algorithm is implemented in multiple threaded environment. The experimental results show the efficiency of this approach

Parallel Computation Models of Particle Swarm Optimization implemented by Multiple Threads

[[abstract]]Particle Swarm Optimization (PSO) is an algorithm motivated by biological systems. However, PSO implementations are sequential, meaning that particles cannot simultaneously interact with other members in the same swarm. This study tries to develop an exact PSO model whose particles simultaneously interact with each other. To model limited communication capability, particles in a swarm are separated into several subgroups. Communication among the subgroups is implemented by parallel computation models based on broadcast, star, migration and diffusion network topologies. Due to the expense and difficulty of true parallel computation, multiple threads are used to model simultaneous particle interaction. We compare the four parallel PSO models and the traditional sequential computation model using measures of convergence error, generations to convergence and execution time. Three experiments to examine the performance of the parallel PSO models are also included