本研究中首先利用基因演算法來探討基因的數量多寡是否能夠縮短得到最佳成本的時間,以及突變率對於機組的影響並以折線圖來表示,再以突變型群優粒子法來探討粒子的數量多寡和強制突變粒子是否能夠縮短得到最佳成本的時間,並把最後搜尋成果以圖示表示,最後再把兩種演算法所得到之結果來做比較,藉此得知兩種演算法對於此研究中最佳解的收斂速度和收斂之效果。
This thesis focuses on the Genetic Algorithm(GA) to inquire into first whether the amount of gene can shorten time of getting the best cost, and mutation rate's influence toward Power Unit and indicate with the line chart. The second part focus on the best cost by Mutation-based Particle Swarm Optimization(MPSO),and then discusses the number of particles and whether forced mutant particles can shorten the time to get the best cost ,and the results are shown in the figure. Finally, the results of the two algorithms are compared, and the effect of the two algorithms on the convergence rate and convergence of the optimal solution in this study is obtained.