Dynamic Optimization of Chemical Processes using Symbiotic Organisms Search Algorithm

Chemical process optimization problems are often modeled as dynamic optimization problems (DOPs). Due to the nonlinear, multimodal and multi-dimensional nature of DOPs, efficient solution of DOPs is a very challenging task. In this paper, a new intelligent search algorithm called symbiotic organisms search (SOS) is proposed for tackling the chemical DOPs. SOS mainly mimics three symbiotic relationships in ecosystem, namely mutualism, commensalism and parasitism, to perform global search. In addition, compared with previous intelligent search algorithms, SOS has the advantages of no tuning parameters, easy to implement and high performance. Combined with control vector parameterization, the proposed SOS is applied to solve five chemical DOPs with different levels of complexity. Simulation results show that SOS can achieve solutions with accuracy comparable to those methods in the literature, and thus can be regarded as an effective tool for the chemical DOPs.