A Switched Parameter Differential Evolution with Multi-donor Mutation and Annealing Based Local Search for Optimization of Lennard-Jones Atomic Clusters
Proceedings IEEE Congress on Evolutionary Computation, CEC 2018
Main objective of this work is to analyze the ability of the Differential Evolution (DE) framework equipped with a multi-donor mutation strategy and annealing-based local search technique to find the global minimum of the potential energy functions, which are used for molecular cluster modeling. Finding such stable molecular clusters is a significant and well-established optimization problem arising from the area of molecular distance geometry and has important implications in artificial drug design as well. Results for moderate (3, 5, 10, 15, 20, 25, and 30 atomic molecules) scale problems are presented here for the Lennard-Jones potential function based atomic clusters. Our experiments reveal that the proposed DE variant is able to yield better results than the competing state-of-art DE based optimizers and the results are with par to the best results listed in the Cambridge energy landscape database (http://doye.chem.ox.ac.uk/jon/structures/LJ.html).
Ghosh, Arka; Mallipeddi, Rammohan; Das, Swagatam; and Das, Asit Kr, "A Switched Parameter Differential Evolution with Multi-donor Mutation and Annealing Based Local Search for Optimization of Lennard-Jones Atomic Clusters" (2018). Conference Articles. 55.