Abstract
This paper proposes a novel meta-heuristic algorithm (MA), called hermit crab optimizer (HCO), which simulates the swarm intelligence of hermit crabs in nature in finding shells protecting and letting them grow in their lifetime. HCO guides search agents separately and in parallel using new solitary and social search operators. It acts similar to a reinforcement learning process, in which the successful agents and failed ones are treated differently, inspired by the group behavior of hermit crabs and environmental characteristics. Computational experiments with well-known test problems confirm HCO's validity, accuracy, robustness, ability to escape local optima, and balance exploration-exploitation.
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