Abstract
Honey bee colonies depend on pollen and nectar from flowers for their feed. The act of searching for this flowers by the bees is called foraging. The foraging behaviour of bees depends on the profitability of nectar and pollen sources as well as the needs of the colony. This behaviour is modeled into an algorithm called Foraging Bee Optimization Algorithm (FBA). After initialization, the algorithm loops through three phases based on bees’ nature foraging behaviour called the 3W: Waggle, Work, and Withdraw. A large number of flowers are initialized randomly in the problem space. During the waggle phase, bees are recruited to patch with profitable nectar sources. In the work phase, new flowers are discovered and memorized by bees. In the withdraw phase bees eliminate unprofitable flowers and recalibrate for recruitment. The proposed FBA is tested on three unimodal and twelve multimodal benchmark. The result is compared with two state-of-the-art natured-inspired optimization algorithm.
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