Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom

Surrounded by an assortment of intelligent and efficient search entities, the Low-Level Hybridization (LLH) for Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are proven to be a comprehensive tool for solving different kinds of optimization problems due to their contradictive behaviour. In addition, the two algorithms have achieved a remarkable improvement from the adaptation of dynamic parameterization. However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. In addition, research has identified that the existing tools are not adequately designed to enable users to easily develop the algorithms with the dynamic parameterization. In responding to this problem, this research investigates rapid mechanisms for the LLH design and development with easy, flexible and concise programming. The research has proposed new implementation frameworks and new scripting language with the dynamic parameterization