To find out the solution, we give the definition of the solution of the Equation 2.1 with initial data (2.2).
Clearly, the (SEP) contains two equilibrium problems, that is, we find out the solution of one equilibrium problem, i.e., its image under a given bounded linear operator, must be the solution of another equilibrium problem.
Input a three tuple (G, O R N0, A R S0), where G is the instance triple graph, O R N0 represents a concrete OWL reasoning network, and A R S0 denotes the associated result set; the reasoner is required to solve the problem: find out the solution domain S = {(O0, OPC, O k )∣(O0, O k ) ∈ A R S0, OPC denotes the OWL property chain that links O0 and O k together.}.
Problem 10 Input a three tuple (G, O R N 0, A R S 0), where G is the instance triple graph, O R N 0 represents a concrete OWL reasoning network, and A R S 0 denotes the associated result set; the reasoner is required to solve the problem: find out the solution domain S = {(O 0, OPC, O k )∣(O 0, O k ) ∈ A R S 0, OPC denotes the OWL property chain that links O 0 and O k together.}.
Bearing this in mind, it is proposed an algorithm which allows for the implementation of GA solution in a real time fashion, taking advantage of its robustness to find out the optimal solution.
The PSO has a strong and reliable capability to find out the optimistic solution.
A cooperative equilibrium searching method is proposed to find out the final solution.
The 200 iterations are performed to find out the best solution.
In view of this difficulty, we try to find out the approximate solution to describe the performance improvement.
In this paper, for each m = 1,2,3,4, we will find out the general solution of the functional equation.
