Most of the optimization methods utilize the upper and lower bounds constraints to handle this issue. At the same time, each variable can significantly affect the direction of the optimization method to find its optimal value. Thus, returning to the lower or/and upper values leads to a delay in finding the optimal values in each iteration.
Your question is not very clear. Do you mean moving into the infeasible space during the algorithm? Some algorithms do that, they don't always maintain a feasible solution throughout the execution of the algorithm. It just depends on context and the type of problem, whether this will be definitely helpful or not cannot be stated in general.
Dear Dr. Cenk,
Thank you so much for answering this question. Yes, definitely I mean that. during the execution of the algorithm and using the exploration and exploitation techniques, in general, the conventional and basic ways will return the upper and/or upper boundaries of each variable. This will lead to losing some of the optimal solutions especially when the parameters that need to be extracted are large. For example, extraction the parameter of the double or three diodes of the PV model. Is there an advanced way that helps the algorithm to find optimal solutions at a less number of generations?
Hussein Mohammed Ridha , It depends on the context and type of the problem as Prof Cenk Çalışkan has told. Not all algorithms always maintain a feasible solution throughout the execution of the algorithm.
Hussein Mohammed Ridha , you may want to take a look at this for the problem you mentioned:
Article Parameter Estimation of Three Diode Photovoltaic Model Using...
Dears Dr. Cenk and Ghosh,
Thank you so much for your response. In fact, I have officially developed a new strategy ehich can handly this issue practically and return optimal solution within feasible ranges rather than upper and/or lower bounds. The results showed execellent performance of the proposed algorithm. The matlab code will be given in recent future.
Best regards,
Hussein
Well, it's certainly true that the constraints imposed by the decision variable range is a major factor for any algorithm to become snarled into a local optimum, but a number of methods have been devised to overcome this limitation. I'm mostly interested in the mechanism presented by the Jellyfish Search (JS) algorithm, for instance, where out-of-bound variables are adjusted in a way where a randomized operator controls its new feasible position (more details can be found in the original paper).
You mentioned that you have developed a new strategy to deal with this issue. Would you mind sharing either the code or article in which it is published?
Thanks in advance.
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