Sardine feast metaheuristic optimization: an algorithm based on sardine feeding frenzy

Abstract

Many metaheuristics mimic biological interaction metaphors, such as ant colony, particle swarm, bee foraging, eagle predator behavior, and cuckoo brood parasitism, to solve complex optimization problems. Another type of biological interaction is commensalism, where one species obtains food from the other without harming or benefiting the latter. One of the great objective-driven commensalism phenomena that amazes scientists and has not yet been modeled is the sardine feast. In this study, we create an optimization algorithm, the sardine feast metaheuristic algorithm (SFMO), based on the ecological relationship between all predators involved in the feast. In this initial work, the algorithm is based on the behavior of dolphins and two types of sea birds, blue-footed boobies and brown pelicans, which prey on a school of sardines. We demonstrate the usefulness of the algorithm for solving several standard benchmark functions and compare the results with those obtained by using another metaheuristic algorithm, namely the Genetic Algorithm (GA), Bat-inspired Algorithm (BA) and Cuckoo Search (CS). The results of the tests show that the SFMO is better in terms of number of evaluations compared with the other algorithms. Further refinement of the model is needed to fully develop the algorithm.