Agarophytic seaweeds have assumed prominence recently due to the development of innovative products, different marketing strategies as well as attracting new entrepreneurs and investors. Several domestic species have emerged as key players aptly supporting regional agar trade. Gracilaria dura is one such example and its commercial farming has been adopted by local Indian fisherman for diversification of their livelihood. This necessitated the adequate and continuous supply of viable seeds for sustainability of farming and subsequent processing sector. We herein reported data centric approach by adopting combined artificial neural network (ANN) model, particle swarm optimization (PSO) as well as response surface methodology (RSM) to optimize salinity, temperature, media concentration and weight to volume ratio to derive an accurate regeneration strategy in clonal seedlings. ANN topology of 4-16-1 and the combination of tangent-sigmoidal transfer function for hidden layer and linear function for output layer was found to be optimal with maximum R-value of 0.991. On employing optimized ANN model as a fitness function with PSO tool, the optimal physiochemical factors were 27 ppt salinity, 25 °C, 2.19 g L⁻¹ DAP and 303 ml media volume. Further, the results of ANN model were experimentally validated and 33.54 ± 6.36% regeneration was observed. The prediction error in optimum regeneration rate by the ANN-PSO and RSM were 1.25% and 13.75%, respectively. The study demonstrated the efficacy of combined ANN-PSO method in solving the nonlinearity of the system.

由于创新产品的开发，不同的营销策略以及吸引新的企业家和投资者，农用海藻最近已成为人们关注的焦点。几种国内物种已成为适当支持区域琼脂贸易的主要参与者。硬cil一个这样的例子，其商业化养殖已被当地印度渔民所采用，以实现其生计的多样化。这就需要持续不断地供应有活力的种子，以实现农业和后续加工业的可持续性。我们在这里报道了通过采用组合人工神经网络（ANN）模型，粒子群优化（PSO）和响应面方法（RSM）来优化盐度，温度，培养基浓度和重量体积比以得出精确再生的数据中心方法育苗策略。发现4-16-1的ANN拓扑以及隐藏层的切线-S型传递函数和输出层的线性函数的组合是最佳的，最大R值为0.991。在使用优化的ANN模型作为PSO工具的适应度函数时，^-1 DAP和303 ml培养基体积。此外，通过实验验证了ANN模型的结果，并观察到33.54±6.36％的再生。ANN-PSO和RSM对最佳再生率的预测误差分别为1.25％和13.75％。研究证明了组合的ANN-PSO方法在解决系统非线性方面的功效。