Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.The authors appreciate the support to the government of the Basque Country through research programs Grants N. ELKARTEK 20/71 and ELKARTEK: KK-2019/00099

Cúmulo de partículas coevolutivo cooperativo usando lógica borrosa para la optimización a gran escala

A cooperative coevolutionary framework can improve the performance of optimization algorithms on large-scale problems. In this paper, we propose a new Cooperative Coevolutionary algorithm to improve our preliminary work, FuzzyPSO2. This new proposal, called CCFPSO, uses the random grouping technique that changes the size of the subcomponents in each generation. Unlike FuzzyPSO2, CCFPSO’s re-initialization of the variables, suggested by the fuzzy system, were performed on the particles with the worst fitness values. In addition, instead of updating the particles based on the global best particle, CCFPSO was updated considering the personal best particle and the neighborhood best particle. This proposal was tested on large-scale problems that resemble real-world problems (CEC2008, CEC2010), where the performance of CCFPSO was favorable in comparison with other state-of-the-art PSO versions, namely CCPSO2, SLPSO, and CSO. The experimental results indicate that using a Cooperative Coevolutionary PSO approach with a fuzzy logic system can improve results on high dimensionality problems (100 to 1000 variables).Un marco coevolutivo cooperativo puede mejorar el rendimiento de los algoritmos de optimización en problemas a gran escala. En este trabajo, proponemos un nuevo algoritmo coevolutivo cooperativo para mejorar nuestro trabajo preliminar, FuzzyPSO2. Esta nueva propuesta, denominada CCFPSO, utiliza la técnica de agrupación aleatoria que cambia el tamaño de los subcomponentes en cada generación. A diferencia de FuzzyPSO2, la reinicialización de las variables de CCFPSO, sugerida por el sistema difuso, se realizaron sobre las partículas con los peores valores de fitness. Además, en lugar de actualizar las partículas basándose en la mejor partícula global, CCFPSO se actualizó considerando la mejor partícula personal y la mejor partícula del vecindario. Esta propuesta se probó en problemas a gran escala que se asemejan a los del mundo real (CEC2008, CEC2010), donde el rendimiento de CCFPSO fue favorable en comparación con otras versiones de PSO del estado del arte, a saber, CCPSO2, SLPSO y CSO. Los resultados experimentales indican que el uso de un enfoque PSO coevolutivo cooperativo con un sistema de lógica difusa puede mejorar los resultados en problemas de alta dimensionalidad (de 100 a 1000 variables).Facultad de Informátic