A New Framework for Metaheuristic Search Based on Animal Foraging

Abstract

In this paper, a new framework for metaheuristic search for global optimization is introduced. It is suitable for continuous nonlinear optimization problems. This framework is mimicking the seal pup behavior and its ability to search and choose the best lair to escape from predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy everytime consists of searching and selecting the best lair. For that, the seal pup performs a random walk to find a new lair. Stimulated by the sensitive nature of seals against external noise, the random walk is based on two search modes, normal mode and urgent mode. In normal mode, the pup moves between closely adjacent lairs via a Brownian walk. In urgent mode, the pup leaves the proximity area far away and performs a Levy walk to find a new lair from sparse targets. The switch between these two modes is realized by the random noise emitted by predators. The proposed framework can efficiently mimic seal pups behavior to find best location and provide a new approach to be used in global optimization problems.