I am working on a resource allocation problem using an SPEA 2 evolutionary algorithm. The problem involves a decision variable where each variable has a different domain e.g. Ei < di where Ei is the allocation to a user and di is individual demands. The problem involves a linear constraint such that sum(Ei) = total resource. The probability of the creation of feasible off-springs after crossover and mutation operators is extremely low. So, we need a repair operator for this purpose. I need guidance for the selection of suitable repair operators and how should I apply that, I mean should we repair all solutions or some percentage. I would appreciate the guidance, comments, or any literature reference.
For repair operator you may have a look at http://mural.maynoothuniversity.ie/10351/1/DO-GeneRepair-2003.pdf
https://s3.amazonaws.com/academia.edu.documents/8648651/tr93-03.pdf?response-content-disposition=inline%3B%20filename%3DGenetic_programming_for_pedestrians.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BABRWUATX2%2F20200321%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200321T112425Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBsaCXVzLWVhc3QtMSJGMEQCICDPJvTkydoMbzqqURByQ8kJDzCyPArzQSL10B9FIgiRAiASUbke3Pr0F7NkxAo%2F5LY7T4in%2F1DvmwxbRvzTBR7gVSq0AwgUEAAaDDI1MDMxODgxMTIwMCIMFDpBL9VL5GKRhkfmKpEDgV0xf0UBKJ1oy4QNYutTKoe8tr8xefoibH%2Bq8EBX09%2FpYktEKxy2QgmJgxo7NYR9C5nQeU1nCrFPXqL951GyVgPe3g6iPuvhUHSQVTDBOZ%2FAZGG9sTnocOxvz2%2FoJj3NDQA54xC2Nzd1%2FLtEbCz0j%2BL%2FDMLdhJZLnpCTw4nL9Gq5AX%2F45Y2cmJPUTfyTbLnbfahU545t9xSYeU5qph0snzXqh3AtXJtWTplBYLoCNn0Ig3288L0Y%2FJvpKIb1HHQLjvQt64mTZIak0TxgmXIFWDBsiQraRa7F7LiB2blEYVgSgzsQ5Urw0CMorEWLqgBbBd1jm3b0kC6npJDMhGBx5e2DAjo7315cPimDgMtKg9jHq7VGo0T5IGjvFl009Z%2FREpmIsAS%2BrLhi27HllsFEZO3ZvZx03lBCh6XUBWwKDbtKXQCTJS3%2Blxc1IrHEsjUj%2FfWhGvsPPtW0amXcnKOq2hqxz2NblBtIpgWb52uAtioYZIUogk%2BNkdNnxwrSXtOIlTsz%2F8sWmr9z1BE9W5cuFbMwmtrX8wU67AHX20yjnV%2BnRdEvcPmT5kVaV0fPBWsuegpWV5QrvkXxpcPPTnmt5I%2FKzSMtNFQBV%2F43QSjO0W27qcsjcpHu41SIfppjhoJNqTawnxIZ7K93REHXQzIZUo%2B7ElSP9kbpWTyEY0aBVaj5kftRQfeUVv4QBbELYwv7GOF8yxpExZC079EzHyZWAqqfBbCUhas1A9qd1JHy9goNMeh5Gogf931xxNdd9Ny8%2Bo%2BDwYw95OjYrs8ipRBWzCIOP0lNtf5m4VAWyPagZMTrwjOB1TsCED99V0Y97XeY7h6gagquPF38iAKyotMIE5EoUfm3vw%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=2f47187fc8832f113710a20222bd5934173fab0a61d7a5487a0ef65a75a6596c
I have published two-three papers on the nonlinear, continuous resource allocation problem. Is your problem based on continuous or integer variables? The integer version is similar to the knapsack problem, which is one the easiest combinatorial that we know, so perhaps your problem is too easy to even think of bothering with a heuristic. Let us know what the problem looks like in detail - otherwise it is impossible to help.
Dr. Michael Patriksson and Dr. Muhammad Ali thank you for the responses. I saw very relevant material in your publications but before I can absorb them and I am describing the problem as best I can. Earlier, I couldn't add details because I did not know how to write an equation in RG.
Actually, I want to optimize energy allocation Ei to users against their individual demands di in a scenario where we have limited energy supply E available s.t E < ∑N di , Ei, di ∈ Rn and E ∈ R. The decision variable Ei has bounds given as Ei
Saifullah Khalid What is the objective function? I do not find it in your description That would otherwise be very handy to have. Also, you need to restrict E_i to be non-negative.
I can - with almost certainty - say that this problem is a standard resource allocation problem, for which there are solvers that guarantee to provide an optimal solution, within a second. (My papers on it describe a great variety of models and approaches.) Hence, skip the metaheuristic, please - I do not think it is going to provide an optimum, while using the simple tools described in my papers will always yield optimal solutions.
- Badges
- Science topic