Roman Denysiuk

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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(More)
Multiobjective approach to optimal control for a tuberculosis model This is a preprint of a paper whose final and definite form will be published in Mathematical modelling can help to explain the nature and dynamics of infection transmissions, as well as support a policy for implementing those strategies that are most likely to bring public health and(More)
A new hybrid evolutionary multiobjective algorithm guided by descent directions-cmmse Abstract 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(More)
Genetic algorithms as most population based algorithms are good at identifying promising areas of the search space (exploration), but less good at fine-tuning the approximation to the minimum (exploitation). Conversely, local search algorithms like pattern search are good at improving the accuracy of that approximation. Thus, a promising idea is combining(More)
In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated(More)
During the last decades, the global prevalence of dengue progressed dramatically. It is a disease that is now endemic in more than one hundred countries of Africa, America, Asia, and the Western Pacific. In this paper, we present a mathematical model for the dengue disease transmission described by a system of ordinary differential equations and propose a(More)
This paper proposes a generalized descent directions-guided multiobjective algorithm (DDMOA2). DDMOA2 uses the scalarizing fitness assignment in its parent and environmental selection procedures. The population consists of leader and non-leader individuals. Each individual in the population is represented by a tuple containing its genotype as well as the(More)
An augmented Lagrangian algorithm is presented to solve a global optimization problem that arises when modeling the activated sludge system in a Wastewater Treatment Plant, attempting to minimize both investment and operation costs. It is a heuristic-based algorithm that uses a genetic algorithm to explore the search space for a global optimum and a pattern(More)
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(More)
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(More)