Rotation-Based Multi-Particle Collision Algorithm with Hooke Jeeves

Abstract

A new variant of the hybrid metaheuristic MPCA-HJ (Multi-Particle Collision
Algorithm with Hooke-Jeeves method) is presented. Multi-Particle Collision Algorithm is a metaheuristic algorithm that performing a traveling on the search space. The addition of the Rotation-Based Learning mechanism to the exploration search enhances the possibility to cover a larger area in the search space. The Hooke-Jeeves direct search method exploites the best solution found by the MPCA, allowing to achieve better solutions. The performance of all implementation are evaluated over twenty-two well known benchmark functions.