Optimizing Environmental Parameters with Intelligent Systems to Enhance Prawn Survival

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Adnan Alam Khan, Syed Asif Ali, Muhammad Zulqarnain Siddiqui, Engr. Shahnila Badar, Osama Ahmed Siddiqui, Maleeha Anwar


This study investigated factors influencing low survival rates of Pacific white shrimp post-larvae (PSL) from hatching to nauplii stage. Particle swarm optimization (PSO) and fuzzy analytic hierarchy process (FAHP) were applied to PSL migration modeling and environmental optimization. PSO simulated nauplii movement towards optimal feeds. FAHP assessed growth based on weighted variables across four ponds. Results demonstrated balancing nutrient diet, temperature, pH, salinity, minerals, and technical pond requirements minimized nauplii distraction and maximized PSL yields. However, birth and mortality imbalances persist, motivating further intelligent optimization to enhance blue economy sustainability and profits. PSO is the only one that can provides the intelligent system about PSL best location, guides about directions, feeds, chemicals and temperature. The proposed system adopts particle PSL characteristics such position, velocity, cost, ideal position, and optimal cost position. The PSL as a particle velocity equation  shows the relationship between particle velocity and the best cost of nature.  The outcome of the system depicts successful PSL. Additional research is needed to elucidate primary causes of nauplii and PSL mortality during early development.


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