PARTICLE SWARM OPTIMIZATION WITH TURBULENCE FACTOR FOR TWO- DIMENSIONAL GUILLOTINE SINGLE KNAPSACK PROBLEMS

This study presents the (un)constrained (un)weighted k-staged fixed and rotated twodimensional guillotineable single knapsack problem. A suitable encoding based on slicing tree is presented. A hybrid algorithm based on the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) is developed. This algorithm used the main characteristics of PSO and introduced the flight turbulence factor through the concept of mutation of GA. The computational results on large sets of test cases show that the methodology has effectiveness and robustness to solve the two-dimensional knapsack problem.