Metaheuristic inspired on owls behavior applied to heat exchangers design

•Metaheuristic inspired on owls behavior for single and multi-objective optimization is proposed.•Heat exchangers design presented better results than previous literature.•Total annual cost of the shell-and-tube heat exchanger case study 1 is decreased by 28.17%.•Total annual cost of the shell-and-tube heat exchanger case study 2 is decreased by 57.78%. The main goal of this paper is to propose a novel simple metaheuristic optimizer relate to the swarm intelligence field based on the decoy behavior of owls that has just five main steps and simple control parameters to tune, called Owl Optimization Algorithm (OOA). The main inspiration of OOA is the decoy behavior of the owls when any kind of danger is detected near the nests. The main reasons of using OOA are its high efficiency with fast convergence speed to single-objective optimization problems. The effectiveness of the proposed OOA is demonstrated on a set of well-known mathematical benchmark functions for single-objective and multi-objective optimization and engineering case studies focusing on heat exchangers. The results to mathematical benchmark functions show that OOA converges quickly to the optimal solution with competitive runtime compared with other algorithms of literature for single-objective functions. For multi-objective functions the results showed that the proposed algorithm reaches competitive performance metrics regarding capacity, convergence, diversity and convergence-diversity. When applied in heat exchangers cases, the OOA achieved better results for the objective functions adopted, total cost for both shell-and-tube heat exchanger case studies for single-objective optimization (with 28.17% and 57.78% of reduction in total cost respectively for each case) and good results in comparison with previous works when applied to multi-objective optimization of heat exchangers.