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Introduction
Publications
Publications (6)
Multimodal optimization can be divided into two main categories. The first focuses on finding only one global optimum, e.g., one peak, and the second focuses on finding multiple global optima, e.g., multiple peaks, and it is the focus of this work. The first category can be approached by single-solution and population-based metaheuristics, while in...
The main difficulty encountered by population-based approaches in multimodal problems is their loss of diversity while converging to an optimum. Also, it is known that parameters play a big role in the performance of metaheuristics. Hence, in this paper two variations of the NCDE algorithm for multimodal optimization are proposed. The first version...
Population-based search algorithms, such as the Differential Evolution approach, evolve a pool of candidate solutions during the optimization process and are suitable for massively parallel architectures promoted by the use of GPUs. Hence, this paper proposes a GPU-based self-adaptive Differential Evolution employing the jDE mechanism to control it...