PeSOA: Penguins Search Optimisation Algorithm for Global Optimisation Problems
Youcef Gheraibia, Abdelouahab Moussaoui, Peng-Yeng Yin, Yiannis, Papadopoulos, and Smaine Maazouzi

TL;DR
PeSOA is a novel metaheuristic inspired by penguin foraging behaviors, combining collaboration and communication strategies to efficiently explore and exploit solution spaces in global optimization problems.
Contribution
This paper introduces PeSOA, a new algorithm inspired by penguin foraging, with innovative communication and migration strategies for improved optimization performance.
Findings
PeSOA outperforms six other metaheuristics on benchmark functions.
The algorithm demonstrates stable performance across different run times.
PeSOA effectively balances exploration and exploitation.
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Robotic Path Planning Algorithms
