estimation of parameters of non-linear regression based on psogsa algorithm

Although computational strategies for taking care of non-linear regression based on hybrid algorithms (epnrha) to estimation of parameters have always been available for years, the further application of evolutionary algorithms (eas) to such difficulties provides a framework for addressing a wide range of multi-objective conflicts (mops). nrpsogsa is an estimation of parameters of non-linear regression gravitational search algorithm with practical swarm optimization that involves the synthesis of hegemony by using the hybrid algorithm (psogsa) approach is utilized. whilst gravitational search algorithm with practical swarm optimization since the leader hiring process uses the tchebycheff strategy as a criterion, simplifying the multi-objective problem (mop) by rewriting it as a set of tchebycheff approach, solving these issues at the same time within the gsa context may lead to rapid resolution. dominance is important in constructing the leader’s library because it allows the chosen leaders to encompass fewer dense places, reducing global optimization problems and producing a more diverse approximated pareto front. 6 non-linear standard functions yielded this result. psogsa appears to be more productive than gsa, pso, and bat. all of the outcomes were completed. by matlab (r2020b).