• PSO Algorithm to generate test cases
A. Initialize the population with N Particles where Program will search for optimal solution through the movement of these particles. And Set iterations counter I = 0.
B. Apply Fitness function: Calculating the fitness value by calculating the percentage of this particle will share in minimizing the total processing time to find the optimal solution.
C. Compare the calculated fitness value of each particle with its (lbest). If current value is better than (lbest), then set the current location as the (lbest) location. Furthermore, if current value is better than (gbest) , then reset (gbest) to the current index in particle array. Select the best particle as (gbest).
D. Where :(lbest)is particle with …show more content…
VII. ANALYSIS AND RESULTS
This paper presented the results of PSO and PPSO. In PSO, the results show inverse relationship between CF & ET, although the optimizing, the cost function elapsed time decreased compared with MCWA. In PPSO found that there is an inverse relationship between CF & ET although optimizing the cost function, but elapsed time decreased compared with MCWA and PSO. This paper shows the relationship between PSO and PPSO where elapsed time in PPSO decreased compared with PSO and shows inverse relationship between SP, PSO and PPSO Whenever an increase in speed occur it decreased in PSO and also time decreased more in PPSO.
VIII. CONCLUSIONS
PSO is relatively recent heuristic approach, it is similar to PPSO in a way that they both are population based evolutionary algorithms. The research presents the application of PSO and PPSO. The proposed research described the basic concepts of PSO and PPSO, explaining the test cases that generated using PSO and PPSO and how they are useful in finding the optimal solution to the problem. Comparative study is done between both the algorithm where PPSO can be useful, and showing how PPSO overcome the drawback of PSO. This paper shows that PPSO algorithm is more efficient in speed and time compared with PSO algorithm to generate test
A. Initialize the population with N Particles where Program will search for optimal solution through the movement of these particles. And Set iterations counter I = 0.
B. Apply Fitness function: Calculating the fitness value by calculating the percentage of this particle will share in minimizing the total processing time to find the optimal solution.
C. Compare the calculated fitness value of each particle with its (lbest). If current value is better than (lbest), then set the current location as the (lbest) location. Furthermore, if current value is better than (gbest) , then reset (gbest) to the current index in particle array. Select the best particle as (gbest).
D. Where :(lbest)is particle with …show more content…
VII. ANALYSIS AND RESULTS
This paper presented the results of PSO and PPSO. In PSO, the results show inverse relationship between CF & ET, although the optimizing, the cost function elapsed time decreased compared with MCWA. In PPSO found that there is an inverse relationship between CF & ET although optimizing the cost function, but elapsed time decreased compared with MCWA and PSO. This paper shows the relationship between PSO and PPSO where elapsed time in PPSO decreased compared with PSO and shows inverse relationship between SP, PSO and PPSO Whenever an increase in speed occur it decreased in PSO and also time decreased more in PPSO.
VIII. CONCLUSIONS
PSO is relatively recent heuristic approach, it is similar to PPSO in a way that they both are population based evolutionary algorithms. The research presents the application of PSO and PPSO. The proposed research described the basic concepts of PSO and PPSO, explaining the test cases that generated using PSO and PPSO and how they are useful in finding the optimal solution to the problem. Comparative study is done between both the algorithm where PPSO can be useful, and showing how PPSO overcome the drawback of PSO. This paper shows that PPSO algorithm is more efficient in speed and time compared with PSO algorithm to generate test