Rotation-Based Multi-Particle Collision Algorithm with Hooke Jeeves

Reynier Hernández Torres, Haroldo de Campos Velho


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.


Hybrid metaheuristic, rotation-based learning, opposition-based learning, multi-particle collision algorithm

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