An efficient derivative-free optimization algorithm inspired by avian life-saving manoeuvres

A novel meta-heuristic algorithm is introduced that is inspired by aerial escaping strategies of a bird to avoid being captured by the hunter. The escaping bird chooses its life-saving maneuver based on the mass and velocity of it and of the predator bird. These flights are simulated in the developed algorithm as movements of artificial search agents. Hunting flight of the artificial predator, plays the role of search intensification; while the escaping prey explores new places in the space by flying in the opposite direction or making a turn. The method adaptively tunes the flight lengths in each pair of the prey/predator agents to avoid use of extra control parameters. It takes advantage of swarm intelligence and opposition-based learning. Twelve test functions as well as seven engi-neering problems in discrete, continuous and mixed discrete-continuous types are successfully solved by the proposed Escaping Bird Search; exhibiting its high convergence rate and competitive performance in comparison with the other treated meta-heuristics.

Automated summary

This study introduces a novel meta-heuristic algorithm inspired by bird's escaping strategies, simulating the aerial confrontation between predator and prey. Adaptive tuning of flight lengths is used to enhance search efficiency, demonstrating high convergence rate and competitive performance compared to other meta-heuristics.