comparing performance of evolutionary algorithms for optimizing synthetic and real-world case studies

The application of meta-heuristic algorithms (mas), for optimizing processes in chemical industries has rapidly grown in recent years# this is mainly driven by the increased cost of raw materials and the economic pressure for reducing the operational costs# it is already known that there is no best method# the performance of mas is case-specific and may change# accordingly, selecting a proper ma for a desired optimization has a special importance# for this reason, a comparison must be made to assess the capabilities of different algorithms# in this study, a fair comparison was made on some benchmark functions and a real-world case study (neural network model of a natural gas to liquids (gtl) process) between three mas including of genetic algorithm (ga), cuckoo search algorithm (csa) and particle swarm optimization (pso)# in doing so, functions were selected with various features and the algorithms were tuned using parameter meta optimization (pmo) method# four performance indicators including of solution quality, accuracy, effort and success were determined for assessment# the results showed that csa has better performance than others in most cases# lower mean solution quality, standard deviation, required time (or iteration) and higher success parameter in most cases indicated the superiority of csa#