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Development of Nature-Inspired Algorithms for Intelligent Manufacturing Systems
Abstract
Intelligent manufacturing systems focus on enhancing product quality while reducing production costs through the optimization of process parameters. Researchers have faced various challenges in engineering design and process optimization, prompting the development of two variants of the Artificial Bee Colony (ABC) algorithm. One variant incorporates Differential Evolution (DE) operators into the standard ABC, resulting in the creation of a new algorithm called Artificial Bee Colony-Differential Evolution (ABC-DE). This hybrid algorithm synergizes the strengths of both ABC and DE algorithms, making the optimization process both efficient and effective. The ABC-DE algorithm is versatile and can be applied to any manufacturing process improvement aimed at optimizing process parameters, improving product quality, and reducing costs. Its application in the manufacturing industry holds significant potential, enabling companies to lower product prices while maintaining high-quality standards, which could have a profound impact on industry competitiveness and customer satisfaction.
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