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Selection of Biodiversity Conservation Areas

Cover Image for Selection of Biodiversity Conservation Areas

The reserves or protected areas have a fundamental role in the biodiversity of the planet. The main objective of the reserves is to protect areas where a large number of animal and plant species coexist, considering also, a set of abiotic factors such as water, soil and sunlight. This research solves the budget-constrained maximal covering location (BCMCL) problem. The aim of BCMCL problem is to maximise the number of species to be protected by the constraints of a limited budget and the costs that have to protect each area.

The BCMCL problem is an NP-hard optimisation problem with a binary domain. For the resolution of BCMCL problem, the authors propose a binary version of African buffalo optimisation (ABO). The tests performed to solve the BCMCL problem have used a set of 12 test instances that have been solved by the algorithm binary ABO. Moreover, eight transfer functions have been applied and experienced in the binary version of ABO. The algorithms migrating birds optimisation, random descent and steepest descent are used to compare the best results obtained by ABO. Finally, the results show that the binary version of ABO has competitive results compared with other algorithms.

This research is published in a journal peer-review in:

Almonacid, B., Reyes-Hagemann, J., Campos-Nazer, J., & Ramos-Aguilar, J. (2017). Selecting a biodiversity conservation area with a limited budget using the binary african buffalo optimisation algorithm. IET Software, 12(2), 96-111. https://doi.org/10.1049/iet-sen.2016.0327

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Cover Image for AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning

AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning

This research aims to have a system that can automatically create-discover evolutionary metaheuristic algorithms. This system was called the AutoMH framework. The AutoMH framework is based on Reinforcement Learning in which various improvements have been made to its architecture. Mainly the concept of reward, which in this research it works by ranking the generated algorithms.

Boris Almonacid