<p>The fitness evaluation mechanisms (FEMs) methods used in the multi and many-objective optimization plays a crucial role in generating the well converged and diverse approximation of the Pareto front. Recently, various FEMs have been proposed and incorporated in the traditional multi and many-objective optimization algorithms to address the different forms of multi and many-objective optimization problems. Even there have been developed various effective FEMs, still these algorithms often face serious scalability issues when applied to large-scale many-objective optimization problems (LSMaOPs). In this paper a many-objective optimization algorithm (MaOA) based on new <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-Dominance and Shift Density FEM, i.e., <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation> shift density evolutionary algorithm <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-SDEA for LSMaOPs is proposed. The proposed <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-SDEA aims to enhance the performance of NSGA-III by exploiting the (<InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-Dominance and Shift Density FEM). To validate the performance of the proposed <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-SDEA, it is tested on several LSMaOPs benchmark problems with 3–10 objectives and 100–500 decision variables. The obtained results compared with some existing approaches. The comparative study demonstrates that the proposed <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-SDEA approach consistently highlight the effectiveness and superiority over the existing approaches in solving the LSMaOPs. Furthermore, we address multi-objective knapsack problems using <InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-SDEA.</p>