Transdermal drug delivery (TDD) is an attractive system to transport drugs or biological compounds into human body, for its apparent benefit of eliminating pain and inconvenient intravenous injections. Microneedle is one example of TDD devices that involves various parameters and complex set of design requirements. Design and analysis of microneedle structure for drug delivery system has been an important issue among researchers especially in biological micro-electro-mechanical system (BioMEMS) field. As MEMS-based microneedles develop in complexity, there is a greater need to reduce the time taken for a MEMS designer to analyze their design before proceed to fabrication process. This study proposed the used of artificial intelligence (AI) methods as a systematic optimization tool to reduce the amount of time taken for designing a microneedle structure. An optimization of microneedle structure is demonstrated by using two techniques of AI which are particle swarm optimization (PSO) and genetic algorithm (GA). A hybrid optimization algorithm has been investigated by combining the PSO with GA to improve computation competency. This combination method is called hybrid PSO-GA (HPSOGA). There are eight design variables of microneedle structure to be optimized in this research. The complex design of microneedle structure considers the shape of microneedle, material used, size of the array, the base of microneedle, the lumen base, the height of microneedle, the height of the lumen, and the height of the drug container or reservoir.
