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Among swarm-intelligence based (SI) algorithms, artificial bee colony (ABC) algorithm that is inspired by the intelligent behavior of honeybees has recently captured much interest from optimization researchers. They have come out with the proposal of ABC variants with the aims to cater the limitations’ of ABC; slow convergence speed on unimodal functions and premature convergence tendency on multimodal functions. This research has recently become the main topic among optimization researchers. Nonetheless, the variants also have their limitations as they cannot solve the problem simultaneously. With the motivation from those inefficiencies, this work presents a new modified ABC variant, referred to as JA-ABC5a as a problem solver. JA-ABC5a is simulated on 27 commonly used benchmark functions prior to evaluating its optimization performance. Upon justifying its robustness, it is being applied to design an optimal digital IIR filter. Key-Words: Artificial bee colony, Benchmark functions, Digital IIR filter, Swarm-intelligence algorithm.

An Augmented Bio-Inspired Algorithm (JA-ABC5a) to Design Optimal Digital IIR Filter