Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution

D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem

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

A dynamic multi-objective evolutionary algorithm based on decision variable classification

The file attached to this record is the author's final peer reviewed version.In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms

Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records

© 2013 IEEE. Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed coevolutionary cloud model. First, the framework of N-populations distributed coevolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the reward of different subpopulations' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent data set. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions is adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator's robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment cortical surfaces of the neonatal brain 3-D-MRI records, and the DCCAEDR shows the superior competitive results, when compared with the representative algorithms