Bio-inspired algorithms that have been introduced by mimicking the biological phenomenon of nature have widely implemented to cater various real-world problems. As example, memetic algorithm, EGSJAABC3 is applied for economic environmental dispatch (EED) optimization, Hybrid Pareto Grey Wolf Optimization to minimize emission of noise and carbon in U-shaped robotic assembly line and Polar Bear Optimization to optimize heat production. The results obtained from their research have clearly portrayed the robustness of bio-inspired algorithms to cater complex problems. This paper highlights the efficiencies of bio-inspired algorithms implemented to cater problem relate to assembly line balancing. This kind of problem is very crucial to counter since it involves minimizing the time of the machines and operators or cost that required optimal task distribution. The outcome of this paper shows the effectiveness of bio-inspired algorithms in solving assembly line balancing problem compared to traditional method.
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