SUMMARYIn this paper, we introduce MRMOGA (Multiple Resolution Multi-Objective Genetic Algorithm), a new parallel multi-objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well-defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state-of-the-art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi-objective optimization problems in parallel, particularly when dealing with large search spaces.
Whereas Multiobjetive Evolutionary Algorithms have reached certain effectiveness in solving many real-world problems, efficiency still remains as an open problem. One choice to reduce the execution time of the Multiobjetive Evolutionary Algorithms is their parallelization. This paper introduces a novel parallel MOEA which is based on the island model with heterogeneous nodes. This algorithm is characterized by encoding the solutions using a different resolution for each island. In this way, the search space is divided into welldefined overlapped regions in decision variable space. 0-7803-9363
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.