ASBO based compositional optimization in combinatorial catalyst

Abstract

The application of different engineering fields in the discovery and development of new materials, especially of new catalyst, is changing the conventional research methodology in materials science.For Heterogeneous catalysts, their catalytic activity and selectivity are dependant on chemical composition, micro structure and reaction conditions. Therefore, it is worth to do the research over the composition of the catalyst and the reaction conditions that will boost its performance.This paper proposes a computational intelligence  approach based on adaptive social behavior optimization (ASBO) for  catalyst composition optimization to enhance the resulting yield or achieving objective maximal.The proposed approach is especially useful in the combinatorial catalysis optimization wherein the fitness function is unknown, in result cost and time can be drastically reduced with intelligent search method instead of applying real time chemical reaction.Challenge of handling higher dimensionality and achieving  a global solution  can be fulfilled by ASBO which is  based on  human behavior under social structure which makes human as a most successful species in nature.Two different mathematical models of the catalyst composition problem, which contains the optimal complexity and represents practical scenarios  have taken to explore the quality of solution. Particle swarm optimization (PSO) which is considered as a successful heuristic method among others has also been applied to get the comparative performance analysis in detail.