particle swarm optimization

This paper presents an approach to modeling the movement of a multi-agent system in a two-dimensional space using a modified Particle Swarm Optimization (PSO) algorithm, adapted to account for the physical properties of the agents. The standard PSO, originally designed for solving optimization problems through swarm behavior, has been extended to simulate the motion of physical objects with defined mass, velocity, and inter-agent interactions.

Entity Resolution  is the task of mapping the records within a database to their corresponding entities.  The entity resolution problem presents a lot of challenges because of the absence of complete information in records, variant distribution of records for different entities and sometimes overlaps between records of different entities.  In this paper, we have proposed an unsupervised method to solve this problem.  The previously mentioned problem is set as a partitioning problem.  Thereafter, an optimization algorithm-based technique is proposed to solve the entity resolution problem.  T

The analysis of the dependency of influence of the tile sizes of iteration space has been represented. It involves program loop operators’ modification during parallelization for multithreading architectures of the computation systems. The particle swarm optimization method has been considered as a method of the minimization program execution time for tiling speed up.