There are two ways to solve it: convert your multiobjectives to single one using weight sum method or use pareto based optimization of you want to solve all the objectives separately.

The popular algorithm to solve multiobjectives optimization is NSGA

You can use multi-objective optimization methods like MO-BBO and MO-PSO.

Check this book: https://academic.csuohio.edu/simond/EvolutionaryOptimization/

In case, you are using MATLAB optimization toolbox, de facto, you can solve 3 or more variable optimization problems. Literally speaking there is no limit for variables:

https://www.mathworks.com/help/optim/ug/optimvar.html

Thanks for your effort.

Appreciated !

You can use the fuzzy multi-objective method to solve the many-objective optimization problem.

check this paper https://www.researchgate.net/publication/331030063_Fuzzy_multi-objective_placement_of_renewable_energy_sources_in_distribution_system_with_objective_of_loss_reduction_and_reliability_improvement_using_a_novel_hybrid_method

In multi objective optimization we need the concept of dominance to said when a solution is better than other (or if none is). The optimal solution of a multi objective optimization problem is known as the Pareto front which is a set of solutions, and not a single solution as is in single/mono objective optimization.

Prof. Kalyanmoy deb is one of the pioneers in the field of evolutionary algorithms and Multi-Objective Optimization Using Evolutionary Algorithms: Kalyanmoy Deb, Deb Kalyanmoy: 9780471873396: Amazon.com: Books is lucidly explained with most merits and some demerits though. Check the review on amazon before you buy.