A kind of dynamic multiobjective based on fuzzy reasoning Population forecast strategy teaching optimization method
A kind of dynamic multiobjective based on fuzzy reasoning Population forecast strategy teaching optimization method
 CN 106,228,232 A
 Filed: 07/07/2016
 Published: 12/14/2016
 Est. Priority Date: 07/07/2016
 Status: Active Application
First Claim
1. dynamic multiobjective based on a fuzzy reasoning Population forecast strategy teaching optimization method, it is characterised in that step bagInclude:
 first, initialization learner population, then, the algorithm following iterative process of entrance;
(1) random initializtion learner population；
(2) school grade based on all learners of policy update decomposed is used；
(3)Assess and update reference point, calculate the adaptive value of learner, the person that chooses Optimal Learning, and renewal learning person'"'"'s neighborhood；
(4) to wholeIndividual learner population carries out environmental change detection；
(5) if environment changes, then tie mutually with onestep prediction according to fuzzy reasoningThe Population forecast strategy closed produces new learner population.
Chinese PRB Reexamination
Abstract
The invention discloses a kind of dynamic multiobjective based on fuzzy reasoning Population forecast strategy teaching optimization method, by random initializtion learner population；Use school grade based on all learners of policy update decomposed；Assess and update reference point, calculate the adaptive value of learner, the person that chooses Optimal Learning, and renewal learning person'"'"'s neighborhood；Whole learner population is carried out environmental change detection；If environment changes, then the Population forecast strategy combined with onestep prediction according to fuzzy reasoning produces new learner population；Judge whether to meet stopping criterion for iteration.The present invention strengthens algorithm to the location of optimum Pareto disaggregation and tracking ability by the Population forecast strategy that fuzzy reasoning combines with onestep prediction, rapidly environmental change can be made a response, multiple target teaching optimizes Population Regeneration, solve and solve the optimum slowfooted problem of Pareto disaggregation, DMOPs has stronger adaptability and robustness.

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No References
7 Claims

1. dynamic multiobjective based on a fuzzy reasoning Population forecast strategy teaching optimization method, it is characterised in that step bagInclude:
 first, initialization learner population, then, the algorithm following iterative process of entrance;
(1) random initializtion learner population；
(2) school grade based on all learners of policy update decomposed is used；
(3)Assess and update reference point, calculate the adaptive value of learner, the person that chooses Optimal Learning, and renewal learning person'"'"'s neighborhood；
(4) to wholeIndividual learner population carries out environmental change detection；
(5) if environment changes, then tie mutually with onestep prediction according to fuzzy reasoningThe Population forecast strategy closed produces new learner population.
 first, initialization learner population, then, the algorithm following iterative process of entrance;

2.
Dynamic multiobjective based on fuzzy reasoning Population forecast strategy the most according to claim 1 teaching optimization method, itsIt is characterised by:  by using based on the strategy decomposed according to the religion stage with levellearning person more new formula more in described step (2)The school grade of new current learner；
Reference point is assessed and updated to school grade according to the learner after updating, calculates studyThe adaptive value of person, is assessed by the size of adaptive value and the person that chooses Optimal Learning, and according to the size renewal learning person of adaptive valueNeighborhood；
Whole learner population is carried out environmental change detection, reappraises the best learner in all learner populations, ifThe preferably achievement of learner changes, then it is assumed that environment changes, and once environment changes, then according to fuzzy reasoning andThe Population forecast strategy that step prediction combines produces new learner population, otherwise, it is determined whether meet stopping criterion for iteration；
ItsIn, the Population forecast strategy combined with onestep prediction according to fuzzy reasoning produces new learner population and refers to;
Conservation environmentLearner population during change, updates memory pond, and uses the kind that fuzzy reasoning based on maximum entropy combines with onestep predictionGroup'"'"'s predicting strategy produces new learner population, returns step (3) and proceeds to calculate.
 by using based on the strategy decomposed according to the religion stage with levellearning person more new formula more in described step (2)The school grade of new current learner；

3.
Dynamic multiobjective based on fuzzy reasoning Population forecast strategy the most according to claim 2 teaching optimization method, itsIt is characterised by:  described religion levellearning person more new formula,
newX_{i}=X_{i}+r*(NTeacher_{i}TF*NMean_{i})+r*(NTeacher_{i}X_{i}), Wherein, X_{i}And newX_{i}It is respectively corresponding states before and after the study of ith learner, Teacher_{i}It it is ith learner placeBest learner in learner neighborhood, Mean_{i}Being that the average in the learner neighborhood of ith learner place is individual, r is randomVector, the element on every dimension is the random number in the range of [0,1], and TF is the religion factor, value 1 or 2； Described levellearning person more new formula,
 described religion levellearning person more new formula,

4.
Dynamic multiobjective based on fuzzy reasoning Population forecast strategy the most according to claim 1 teaching optimization method, itsBeing characterised by, described assessment also updates reference point and refers to:  assesses according to the school grade of all learners after updating and updatesReference point；
The formula of renewal reference point;
Represent the maximum of ith object function, z^{*}It it is mMaximal solution set, f_{i}X () is the ith target function value of learner x, m is the number of object function.
 assesses according to the school grade of all learners after updating and updatesReference point；

5.
Dynamic multiobjective based on fuzzy reasoning Population forecast strategy the most according to claim 4 teaching optimization method, itsBeing characterised by, the formula of the adaptive value of described calculating learner is as follows:

6.
Dynamic multiobjective based on fuzzy reasoning Population forecast strategy the most according to claim 2 teaching optimization method, itsIt is characterised by:  the new learner population of described generation utilizes below equation to calculate;
 the new learner population of described generation utilizes below equation to calculate;

7.
Dynamic multiobjective based on fuzzy reasoning Population forecast strategy the most according to claim 6 teaching optimization method, itsBeing characterised by, described fuzzy membership, it is as follows that function asks for fuzzy inference rule:
Specification(s)