Comparison Between the Performance of Genetic and Particle Swarm Optimization Algorithm in Order to Solve the Knapsack Problem (original) (raw)
Knapsack problem, is one the most important issues in decision-making and optimization problems, especially in which time and investment has a great importance rate. Genetic Algorithm (GA) and Particle Swarm Optimization algorithm (PSO) can be mentioned as a solution to this problem. GA is one of evolutionary algorithms, that in each of all possible combinations, the best solutions are selected in order to optimize the cost-function. Also PSO algorithm opimized the nonlinear function based on the social behavior of birds. In this paper, by implementing two algorithms, GA and PSO, the process of solving the knapsack problem is comapred. The results of this study, showed that while the GA has a higher speed to converge to an answer, but the efficiency and accuracy of PSO is higher than GA algorithm.
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