Hi Dhanesh,
When deciding between 'paretosearch' and 'gamultiobj' for solving multi-objective optimization problems, consider the following factors:
- 'gamultiobj' is based on genetic algorithms. It is well-suited for complex and non-differentiable objective functions. 'paretosearch' uses a pattern search method and might be more efficient for problems where gradient information is absent but the problem is less complex.
- 'gamultiobj'can provide a broader diversity of solutions in the Pareto front due to its genetic algorithm nature, which explores a wider solution space through mechanisms like crossover and mutation. 'paretosearch' may not explore the solution space as thoroughly as 'gamultiobj', potentially leading to less diversity in the Pareto front.
- 'gamultiobj' might require more computational resources due to its evolutionary approach than the 'paretosearch'.
- 'paretosearch' can be more efficient than 'gamultiobj' for certain types of problems, particularly with fewer local optima.
Please refer the below documentation for 'Compare paretosearch and gamultiobj': https://www.mathworks.com/help/gads/compare-paretosearch-gamultiobj.html
The choice between 'paretosearch' and 'gamultiobj' largely depends on the specific characteristics of the optimization problem at hand.
Please refer the below documentations for 'paretosearch' and 'gamultiobj':
- https://www.mathworks.com/help/gads/paretosearch.html
- https://www.mathworks.com/help/gads/gamultiobj.html
I hope it helps!
