Genetic algorithm for cost optimization of modified multi-component binders

This paper describes the application of a genetic algorithm for the cost optimization of a modified multi-component binder (MMCB). An MMCB comprised of Portland cement (NPC), finely ground mineral additives (fly ash, ponded ash or granulated blast furnace slag), and a highly reactive powder component (usually silica fume, SF) was modified by a superplasticizer (SP). Strength models based on the experimental results were developed. The present work is oriented to the minimization of the MMCB cost for specific strength levels with the help of a changing range genetic algorithm (CRGA) to handle the nonlinear constraints imposed by the MMCB models. The developed CRGA is based on an approach that adaptively shifts and shrinks the size of the search space to the feasible region. The application of CRGA helps to minimize the cost of MMCB with a low resolution of the binary representation scheme and without additional computational efforts.