Isabel Espírito Santo
University of Minho
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Featured researches published by Isabel Espírito Santo.
Applied Mathematics and Computation | 2012
Lino Costa; Isabel Espírito Santo; Edite Manuela da G. P. Fernandes
Abstract Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to reduce the total number of function evaluations, local and global techniques may be combined. Moreover, the hybridization may provide a more effective trade-off between exploitation and exploration of the search space. In this study, we propose a new hybrid genetic algorithm based on a local pattern search that relies on an augmented Lagrangian function for constraint-handling. The local search strategy is used to improve the best approximation found by the genetic algorithm. Convergence to an e -global minimizer is proved. Numerical results and comparisons with other stochastic algorithms using a set of benchmark constrained problems are provided.
genetic and evolutionary computation conference | 2013
Roman Denysiuk; Lino Costa; Isabel Espírito Santo
In this paper, we propose an evolutionary algorithm for handling many-objective optimization problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept of Pareto dominance coupled with the inverted generational distance metric to select the population of the next generation from the combined multi-set of parents and offspring. Furthermore, we suggest a strategy for the restriction of the difference vector in DE operator in order to improve the convergence property in multi-modal fitness landscape. We compare MyO-DEMR with other state-of-the-art multiobjective evolutionary algorithms on a number of multiobjective optimization problems having up to 20 dimensions. The results reveal that the proposed selection scheme is able to effectively guide the search in high-dimensional objective space. Moreover, MyO-DEMR demonstrates significantly superior performance on multi-modal problems comparing with other DE-based approaches.
Optimization | 2013
Lino Costa; Isabel Espírito Santo; Pedro Oliveira
In global optimization with evolutionary algorithms constraint handling presents major difficulties, especially in the case of equality constraints. Several techniques have been proposed to overcome this difficulty. In this work an adaptive constraint handling technique is studied on a set of test problems with two evolutionary algorithms. The results indicate that the proposed adaptive technique produces results with better quality in terms of objective function values and constraint violations. The comparison was assessed by performance profiles based on a new metric that considers information both on objective function value and constraints violation.
genetic and evolutionary computation conference | 2011
Lino Costa; Isabel Espírito Santo; Pedro Oliveira
Optimization with stochastic algorithms has become a relevant approach, specially, in problems with complex search spaces. Due to the stochastic nature of these algorithms, the assessment and comparison is not straightforward. Several performance measures have been proposed to overcome this difficulty. In this work, the use of performance profiles and an analysis integrating a trade-off between accuracy and precision are carried out for the comparison of two stochastic algorithms. Traditionally, performance profiles are used to compare deterministic algorithms. This methodology is applied in the comparison of two stochastic algorithms - genetic algorithms and simulated annealing. The results highlight the advantages and drawbacks of the proposed assessment.
Journal of Mathematical Modelling and Algorithms | 2013
Roman Denysiuk; Lino Costa; Isabel Espírito Santo
Hybridization of local search based algorithms with evolutionary algorithms is still an under-explored research area in multiobjective optimization. In this paper, we propose a new multiobjective algorithm based on a local search method. The main idea is to generate new non-dominated solutions by adding a linear combination of descent directions of the objective functions to a parent solution. Additionally, a strategy based on subpopulations is implemented to avoid the direct computation of descent directions for the entire population. The evaluation of the proposed algorithm is performed on a set of benchmark test problems allowing a comparison with the most representative state-of-the-art multiobjective algorithms. The results show that the proposed approach is highly competitive in terms of the quality of non-dominated solutions and robustness.
genetic and evolutionary computation conference | 2015
Roman Denysiuk; Lino Costa; Isabel Espírito Santo
Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.
parallel problem solving from nature | 2014
Roman Denysiuk; Lino Costa; Isabel Espírito Santo
This paper discusses a selection scheme allowing to employ a clustering technique to guide the search in evolutionary many-objective optimization. The underlying idea to avoid the curse of dimensionality is based on transforming the objective vectors before applying a clustering and the selection of cluster representatives according to the distance to a reference point. The experimental results reveal that the proposed approach is able to effectively guide the search in high-dimensional objective spaces, producing highly competitive performance when compared with state-of-the-art algorithms.
Operations Research Proceedings | 2011
Lino Costa; Isabel Espírito Santo; Edite Manuela da G. P. Fernandes
When modeling an activated sludge system of a wastewater treatment plant (WWTP), several conflicting objectives may arise. The proposed formulation is a highly constrained bi-objective problem where the minimization of the investment and operation costs and the maximization of the quality of the effluent are simultaneously optimized. These two conflicting objectives give rise to a set of Pareto optimal solutions, reflecting different compromises between the objectives. Population based algorithms are particularly suitable to tackle multi-objective problems since they can, in principle, find multiple widely different approximations to the Pareto-optimal solutions in a single run. In this work, the formulated problem is solved through an elitist multi-objective genetic algorithm coupled with a constrained tournament technique. Several trade-offs between objectives are obtained through the optimization process. The direct visualization of the trade-offs through a Pareto curve assists the decision maker in the selection of crucial design and operation variables. The experimental results are promising, with physical meaning and highlight the advantages of using a multi-objective approach.
international conference on evolutionary multi-criterion optimization | 2015
Roman Denysiuk; Lino Costa; Isabel Espírito Santo; José C. Matos
The need to perform the search in the objective space constitutes one of the fundamental differences between multiobjective and single-objective optimization. The performance of any multiobjective evolutionary algorithm (MOEA) is strongly related to the efficacy of its selection mechanism. The population convergence and diversity are two different but equally important goals that must be ensured by the selection mechanism. Despite the equal importance of the two goals, the convergence is often used as the first sorting criterion, whereas the diversity is considered as the second one. In some cases, this can lead to a poor performance, as a severe loss of diversity occurs.
Statistics, Optimization and Information Computing | 2015
Roman Denysiuk; Helena Sofia Rodrigues; M. Teresa T. Monteiro; Lino Costa; Isabel Espírito Santo; Delfim F. M. Torres
During the last decades, the global prevalence of dengue progressed dramatically. It is a disease which is now endemic in more than one hundred countries of Africa, America, Asia and the Western Pacific. This study addresses a mathematical model for the dengue disease transmission and finding the most effective ways of controlling the disease. The model is described by a system of ordinary differential equations representing human and vector dynamics. Multiobjective optimization is applied to find the optimal control strategies, considering the simultaneous minimization of infected humans and costs due to insecticide application. The obtained results show that multiobjective optimization is an effective tool for finding the optimal control. The set of trade-off solutions encompasses a whole range of optimal scenarios, providing valuable information about the dynamics of infection transmissions. The results are discussed for different values of model parameters.