Abstract
Aiming at the characteristics of multi-dimensional knapsack problem, the widely used bare bones particle swarm optimization algorithm is discretized, and the traditional objective function based on large penalty parameters is improved. The infeasible solution is transformed into a feasible solution by using the repair mechanism. The typical examples of 10 multi-dimensional knapsack problems are simulated and compared with four intelligent optimization algorithms. The simulation results show that the discrete bare bones particle swarm optimization algorithm has good convergence efficiency, high precision and good robustness when solving multi-dimensional knapsack problem.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
