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

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 d...

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Veröffentlicht in:Selecciones matemáticas : revista científica del Departamento Académico de Matemáticas Jg. 6; H. 2; S. 156 - 177
Hauptverfasser: Hernández, Reynier, Campos, Haroldo, P. da Luz, Eduardo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Universidad Nacional de Trujillo 30.12.2019
Schlagworte:
Algoritmo de Colisión de Múltiples partículas
Algoritmo estocástico
Aprendizaje basado en oposición
Aprendizaje basado en reflexión
Based Learning
Metaheuristic
Metaheurística
Multi
multi-particle collision algorithm
Opposition
opposition-based learning
Particle Collision Algorithm
Reflection
reflection-based learning
Stochastic algorithm
ISSN:2411-1783, 2411-1783
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Zusammenfassung: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.
ISSN:2411-1783
2411-1783
DOI:10.17268/sel.mat.2019.02.03