User:Nehanazneensiddiqui/sandbox

Adaptive Spider Net Search Algorithm

Adaptive Spider Net Search (ASNS) Algorithm is proposed by Saifullah Khalid for combinatoric optimization issues. Non-linear continuous optimization issues need a robust search methodology to resolve them and this new adaptive Spider Net Search (ASNS) Algorithm has been developed for them.

In the ASNS algorithm, an eternal search space has been discretized and back-tracking and adaptive radius features are utilized to lift the performance of the search method. ASNS Algorithm searches the optimum value of the proportional integral controller parameters i.e. Kp and Ki and therefore the objective function (OF) is determined such as to give their optimum value with the conditions of % overshoot, rise time and settling time. Objective function has an equation that has 3 variables i.e. % overshoot, rise time and settling time. Initially, the Boundary of Kp and Ki, their higher limits and lower limits, then radius value, conditions for ASNS backtracking, objective function and stop criteria has been outlined. Net of spider has been supposed of the shape of the hexagon. 500 hexagons may be used and at every corner of the hexagon, we have defined some random values of Kp & Ki which will be within the range of predefined initial values. Best value of each hexagon will be saved as first corner value of next hexagon. The comparison will move in clockwise as well as zigzag direction for the complete check of optimum value. After every comparison, best value will be compared with next value on the next corner and then the best outcome will be saved and will be compared to next one. This process will repeat itself and will stop when stopping criteria fulfills. We have considered 500 hexagons as shown in figure 1. An observation has been made that comparison of each hexagon corner values goes through nine times as shown in figure 2 and that is the reason for selecting maximum Searching iteration (4500 iterations) for ASNS as the stop criterion. There is a predefined list named as Spider netlist, which contains the values which have been distributed over the corners of the hexagons. Figure 3 shows the flow chart for the search of parameters using Adaptive Spider Net Search (ASNS) Algorithm. This algorithm is extremely convenient to use because of the programming and fewer computational time. The feasibleness and good thing about the algorithm are proved by the simulation results. It’s in no time. The parameters i.e. Kp and Ki have been set at random at first and so it has been tuned by using this algorithm offline. Standard equations of Kp and Ki using settling time (TSettling), rise time (TRise) and percent overshoot (P.O.) are used for locating the objective function within the program. There has been a counter used, which will count the number of iterations and, therefore, the program will stop automatically once the count is up to 4500 i.e. stopping criteria is 4500 iterations.