Enhancement of the Multi-Particle Collision Algorithm by mechanisms derived from the Opposition-Based Optimization

• Autores: Hernández Torres, Reynier, Campos Velho, Haroldo F., P. da Luz, Eduardo F.
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Universidad Nacional de Trujillo
• Repositorio: Revistas - Universidad Nacional de Trujillo
• Lenguaje: inglés
• OAI Identifier: oai:ojs.revistas.unitru.edu.pe:article/2625
• Enlace del recurso: https://revistas.unitru.edu.pe/index.php/SSMM/article/view/2625
• Nivel de acceso: acceso abierto
• Materia: Stochastic algorithm; Metaheuristic; Opposition-Based Learning; Reflection-Based Learning; Multi-Particle Collision Algorithm; Algoritmo estocástico; Metaheurística; Aprendizaje basado en oposición; Aprendizaje basado en reflexión; Algoritmo de Colisión de Múltiples partículas

New versions of the metaheuristic Multi-Particle Collision Algorithm (MPCA) are presented. In order to provide more effective candidate solutions for an optimization problem, the concept of opposition and reflection is introduced to improve the capacity to find a solution in the search space. Four different strategies to compute the reflected and opposite points are implemented. The performance of all implementations is evaluated over thirty objective functions with different complexities, using serial and parallel versions of the algorithms.