Quasi-Random Sampling-Enhanced Metaheuristic Algorithms for CAMD-Based Solvent Selection in Octacosanol Extraction

This study develops a computer-aided molecular design (CAMD) framework to select solvents for extracting octacosanol from multicomponent sugarcane wax. The solvent-selection problem is formulated as a mixed-integer nonlinear programming problem in which candidate solvents are generated from UNIFAC functional groups and evaluated using the net distribution coefficient and net solvent selectivity. Four metaheuristic solvers—ant-colony optimization (ACO), simulated annealing (SA), efficient ant-colony optimization (EACO), and efficient simulated annealing (ESA)—are compared to assess solvent quality and computational efficiency. EACO and ESA incorporate Hammersley sequence sampling to improve multidimensional sampling uniformity relative to conventional pseudo-random sampling. The results show that all four solvers consistently identify the same highest-ranked candidate solvent. ACO and EACO require substantially fewer objective-function evaluations than SA and ESA, while SA-based methods provide greater diversity among lower-ranked candidates. EACO reduces the computational cost relative to ACO while retaining the same top-ranked solvents, whereas ESA provides only a modest efficiency improvement over SA. Overall, the proposed CAMD framework reliably identifies promising solvents for nutraceutical extraction, and quasi-random sampling improves the efficiency of metaheuristic solvent design.