MOMRFO: Multi-objective Manta ray foraging optimizer for handling engineering design problems

Despite its recent appearance, manta ray foraging optimizer (MRFO) has shown a good ability to deal with single-objective real-world problems, which makes its application in solving problems with multiple objectives an interesting direction. Accordingly, the current paper investigates the MRFO optimizer to develop a new algorithm for handling multi-objective engineering design problems. To achieve this goal, the elitist concept is adopted to save the set of Pareto solutions through the integration of an external archive into the standard MRFO. This archive is considered also as a repository from which a search agent is chosen based on its density degree to control the convergence and diversity of manta rays population. Our algorithm’s efficiency is first validated via extensive experiments on ten test functions, and the results were very satisfactory in terms of convergence and diversity, in almost all cases. Then, it is applied to four multi-objective engineering problems, and it showed a good promise in solving real-world problems with multiple objectives.