  This paper develops Penguin search Optimisation Algorithm (PeSOA), a new
metaheuristic algorithm which is inspired by the foraging behaviours of
penguins. A population of penguins located in the solution space of the given
search and optimisation problem is divided into groups and tasked with finding
optimal solutions. The penguins of a group perform simultaneous dives and work
as a team to collaboratively feed on fish the energy content of which
corresponds to the fitness of candidate solutions. Fish stocks have higher
fitness and concentration near areas of solution optima and thus drive the
search. Penguins can migrate to other places if their original habitat lacks
food. We identify two forms of penguin communication both intra-group and
inter-group which are useful in designing intensification and diversification
strategies. An efficient intensification strategy allows fast convergence to a
local optimum, whereas an effective diversification strategy avoids cyclic
behaviour around local optima and explores more effectively the space of
potential solutions. The proposed PeSOA algorithm has been validated on a
well-known set of benchmark functions. Comparative performances with six other
nature-inspired metaheuristics show that the PeSOA performs favourably in these
tests. A run-time analysis shows that the performance obtained by the PeSOA is
very stable at any time of the evolution horizon, making the PeSOA a viable
approach for real world applications.
