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A comparative study of Nature-Inspired Optimization Algorithms (NIOAs)

Overweg, Jef
(2024)

Files

Overweg_76632200_2024.pdf
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  • 1.96 MB

Details

Supervisors
Deville, Yves
Faculty
Ecole polytechnique de Louvain
Degree label
Master [60] en sciences informatiques
Abstract
Nature carries out tasks in simple yet efficient and logical ways, it wouldn't be far-fetched to take inspiration from them to solve various problems. As elementary as it may look, natural processes are composed of complex sub-processes from which we can draw ingenuity from. Nature-Inspired Optimization Algorithms (NIOAs) take inspiration from nature itself to address complex optimization challenges. We will study some specific NIOAs, like genetic algorithms, ant colony optimization, simulated annealing, and the flower pollination algorithm applied to the Traveling Salesman Problem (TSP). The main goal of this master's thesis is to examine in detail the different NIOAs. We will be able to investigate their ability to imitate the adaptive strategies observed in nature and how close to the reality they represent them. And see how effective they are in solving the TSP after we study each algorithm's performance in detail. In the end, this study explores NIOAs, their meaning, and where we will be able to evaluate these algorithms through different analyses. In the end, the research may offer useful insights for determining which algorithm has its own advantages for solving the TSP.