COMPUTATIONAL DEVICE IMPLEMENTED METHOD OF SOLVING CONSTRAINED OPTIMIZATION PROBLEMS

• US 20130110751A1
• Filed: 09/15/2012
• Published: 05/02/2013
• Est. Priority Date: 10/31/2011
• Status: Abandoned Application

• Current Assignee: Taif University
• Original Assignee: Taif University
• Inventors: Aljahdali, Sultan Hamadi, Turabieh, Hamza Ibrahiem, Sheta, Alaa Fathy
• Application Number:
  US13/621,226
• Publication Number:
  US 20130110751A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

A computational device implemented method utilizes a genetic algorithm and modifies the offspring of the genetic algorithm that fall outside of the feasible search space after crossover so that the offspring will be within the feasible search space. To place the offspring in the feasible search space, NFC and HSQPC mechanisms are used.

Claims

1. A computational device implemented method of solving constrained optimization problems, comprising the steps of:

generating an initial population composed of individuals;

determining a fitness value for each individual based on a fitness function;

evaluating a convergence criterion of each individual;

selecting a plurality of individuals;

applying a crossover operator to the plurality of individuals;

determining if each of the plurality of individuals that have had the crossover operator applied to them is in a feasible search space;

applying a mutation operator to an individual in the feasible search space; and

updating a population to obtain an updated population.

2. The computational device implemented method of solving constrained optimization problems provided in claim 1, further comprising the step of applying a HSQPC mechanism if it is determined that that at least one of the individuals that have had the crossover operator applied to them is not in a feasible search space.

3. The computational device implemented method of solving constrained optimization problems provided in claim 2, further comprising the step of applying a second HSQPC mechanism if the previous application of the HSQPC mechanism did not produce an individual in the feasible search space.

4. The computational device implemented method of solving constrained optimization problems provided in claim 1, further comprising the step of applying an NCP mechanism if it is determined that that at least one of the individuals that have had the crossover operator applied to them is not in a feasible search space.

5. The computational device implemented method of solving constrained optimization problems provided in claim 4, further comprising the step of applying a second NCP mechanism if the previous application of the NCP mechanism did not produce an individual in the feasible search space.

6. The computational device implemented method of solving constrained optimization problems provided in claim 1, wherein the initial population must be in the feasible search space.

7. The computational device implemented method of solving constrained optimization problems provided in claim 1, further comprising the step of setting genetic algorithm parameters.

8. The computational device implemented method of solving constrained optimization problems provided in claim 1, further comprising the step of setting problem parameters.

9. The computational device implemented method of solving constrained optimization problems provided in claim 1, further comprising evaluating the updated population for satisfaction of the convergence criterion.

10. The computational device implemented method of solving constrained optimization problems provided in claim 9, further comprising the step of writing a file that contains results that will be used for statistics about a solution after each generation.

11. A computational device implemented method of solving constrained optimization problems, comprising the steps of:

running a genetic algorithm; and

determining if an offspring is in a feasible search space and does not satisfy a convergence criterion.

12. The computational device implemented method of solving constrained optimization problems provided in claim 11, further comprising the step of applying a HSQPC mechanism if it is determined that the offspring is not in the feasible search space and does not satisfy a convergence criterion to obtain an updated offspring.

13. The computational device implemented method of solving constrained optimization problems provided in claim 12, further comprising the step of applying the HSQPC mechanism to the updated offspring if the updated offspring is not in the feasible search space.

14. The computational device implemented method of solving constrained optimization problems provided in claim 11, further comprising applying a NCP mechanism if it is determined that the offspring is not in the feasible search space and does not satisfy a convergence criterion to obtain an updated offspring.

15. The computational device implemented method of solving constrained optimization problems provided in claim 14, further comprising the step of applying the NCP mechanism to the updated offspring if the updated offspring is not in the feasible search space.

16. The computational device implemented method of solving constrained optimization problems provided in claim 11, wherein an initial population of the genetic algorithm is in the feasible search space.

17. The computational device implemented method of solving constrained optimization problems provided in claim 11, further comprising setting the genetic algorithm parameters.

18. The computational device implemented method of solving constrained optimization problems provided in claim 11, further comprising setting the problem parameters.

19. A computational device implemented method of solving constrained optimization problems, comprising the steps of:

running a genetic algorithm;

determining an offspring is not in a feasible search space and does not satisfy a convergence criterion; and

applying either a HSQPC mechanism or a NCP mechanism.

20. The computational device implemented method of solving constrained optimization problems described in claim 19, wherein an initial population of the genetic algorithm is in the feasible search space.