Optimization Algorithms for Library Materials Acquisition


Particle swarm optimization (PSO) is a population-based stochastic approach for solving continuous and discrete optimization problems. In particle swarm optimization, simple software agents, called particles, move in the search space of an optimization problem. The position of a particle represents a candidate solution to the optimization problem at hand. Each particle searches for better positions in the search space by changing its velocity according to rules originally inspired by behavioral models of bird flocking. Particle swarm optimization belongs to the class of swarm intellegence techniques that are used to solve optimization problems.

Project Description

This project is a continuation of last semester's project, see 2016 Research Day Conference paper.

The project involves Discrete Particle Swarm Optimization and its use of particle flocking to find the optimal solution for a maximum budget rate and selection preference of academic library materials and its students. The project seeks to locate a better optimization model by enhancing simulated annealing, tabu search, genetic algorithm and the hill climb algorithm to find the maximum budget rate and selection preference of library materials.

This project can determine if DPSO is in fact the best optimization or our enhanced algorithm based on the previously stated algorithms. It is hypothesized that tabu Search will work more efficiently with better outcomes than DPSO. Each optimization has to be enhanced to work as the DPSO does.

The project is currently underway and the DPSO code has been produced as well as the simulated annealing. It was found that SA and DPSO run together created less convergence and a better outcome. Overall, code has to be wrote for each of the algorithms and modified to fit our parameters or variables.