An Efficient Multi-Objective Shuffled Frog Leaping Algorithm-Based Method for Metabolic Pathway Design

Metabolic pathway design is crucial in synthetic biology. Compared to traditional manual methods, evolutionary computation-based approaches greatly reduce time and cost, becoming the dominant trend. Current algorithms fall into two categories: single-objective and multi-objective. Multi-objective methods outperform single-objective ones in diversity and convergence, making them better suited for complex problems by balancing multiple performance metrics. However, existing multi-objective evolutionary algorithms for pathway design remain inefficient. To address this, we propose an efficient method based on a multi-objective shuffled frog leaping algorithm. Experiments show our approach significantly reduces computation time and discovers higher-performing pathways compared to existing methods.