PeSOA: Penguins Search Optimisation Algorithm for Global Optimisation Problems

PeSOA:Penguins Search Optimisation Algorithm

for Global Optimisation Problems

Youcef Gheraibia1, Abdelouahab Moussaoui2, Peng-Yeng Yin3, Yiannis Papadopoulos1, and Smaine Maazouzi4

1School of Engineering and Computer Science, University of Hull, U.K

2Department of Computer Science, University of Setif 1, Algeria

3Department of Information Management, National Chi Nan University, Taiwan

4Department of Computer Science, Univsersité 20 Août 1955, Algeria

Abstract: 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.

Keywords: Population-based approach, complex problems, intensification strategy, diversification strategy, penguins search.

Received October 1, 2015; accepted March 3, 2016 
 
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