Meta-Heuristic Algorithms for the Generalized Extensible Bin Packing Problem With Overload Cost

In this paper, we consider a generalized the extensible bin packing problem with overload cost, first proposed by Denton et al. [1] in 2010, which total size of items packed into is allowed to exceed its capacity, and cost incurred each equal fixed plus objective minimize all bins. According characteristics problem, propose an improved ant colony optimization algorithm (IACO), enhances positive feedback effect ACO improving update method pheromone adaptive adjustment parameters. We also introduce variable neighborhood search improve convergence get rid phenomenon local extrema. Then, present discrete particle swarm (DPSO) solve problem. order ensure uniform distribution high quality initial swarm, use some heuristic methods initialization process so that can cover entire space large probability, effectively improves performance DPSO algorithm. Finally, compare analyze these algorithms through two sets computational experimental frameworks. Compared literature, results signify MDPSO are more competitive than other metaheuristic algorithms..