Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection optimum process parameter levels in any process, numerous metaheuristic algorithms have been proposed so far. However, many are either computationally too expensive or become trapped pit local optima. counter these challenges, this paper, hybrid called PSO-GSA employed that works by combining iterative improvement capability particle swarm (PSO) gravitational search algorithm (GSA). A binary PSO also fused with GSA to develop BPSO-GSA algorithm. Both i.e., BPSO-GSA, compared against traditional algorithms, such as tabu (TS), genetic (GA), differential evolution (DE), algorithms. Moreover, another popular DE-GA used comparison. Since earlier already studied performance on mathematical benchmark functions, two real-world-applicable independent case studies biodiesel production considered. Based extensive comparisons, significantly better solutions observed outcomes work will be beneficial similar rely polynomial models.