Improved Slime mould algorithm using logical chaos perturbation and reference point non-dominated sorting for multi-objective optimization

Slime Mould Algorithm (SMA) has been widely noticed by researchers for its powerful multi-point search capability and simple feasible structure, many advanced versions of SMA have also proposed. However, most existing methods focus on the single-objective research domain, multi-objective remains relatively scarce; moreover, basic lacks global capability, extension to domain easily leads loss solution diversity. Therefore, in this paper, we propose a general framework which utilises logical chaotic single-dimensional perturbation mechanism increase traversal individuals decision space; secondly, non-dominated sorting based reference point is used select more diverse solutions participate next generation evolution. Through experiments with seven algorithms such as CMOPSO, NSGA-II, NSGA-III, MOEAD, PSEA-II, SPEA-II NSLS 28 basis functions, results show that achieves best convergence, accuracy