<![CDATA[Bioethanol is a promising renewable energy source, and microalgae such as Chlorella vulgaris and Spirulina platensis offer high productivity potential. This work applies a Genetic Algorithm (GA) to optimize key environmental parameters—pH, light intensity, and temperature—within a simulation framework over a 100-day cultivation period. GA optimization resulted in a 25% increase in total ethanol yield, from baseline values of 51.00 to 63.66 g/L for Chlorella and 32.64 to 40.79 g/L for Spirulina. We benchmarked GA against Particle Swarm Optimization (PSO), Differential Evolution (DE), and Simulated Annealing (SA); GA consistently delivered superior convergence and final yields. The model incorporates phase‑dependent carbohydrate accumulation and realistic environmental disturbances, though biological complexities such as photoinhibition and nutrient limitations are acknowledged as future work. To enable meaningful convergence, the growth model was extended with mild photoinhibition and nutrient limitation terms, ensuring a more realistic fitness landscape. Findings support the viability of metaheuristic optimization in microalgae biofuel systems and indicate potential for intelligent control integration in photobioreactor operations.]]>
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