Effects of memory and genetic operators on Artificial Bee Colony algorithm for Single Container Loading problem

Abstract

The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Three-Dimensional Bin Packing Problem (3DBPP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop two different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a generated 3DBPP dataset to observe the effects of the memory mechanism and the genetic operators separately. The results show that the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm, for solving heterogeneous 3DBPPs.

Multi-period uncertain portfolio selection model with prospect utility function

“…Liu et al [ 37 ] introduced an improved ABC that enhances the exploration ability by modifying the behaviors of bees. Bayraktar et al [ 38 ] improved the exploitation and exploration ability of the ABC by ultilizing memory mechanisms and genetic operators to produce three imporved ABC algorithms. Singh and Sundar [ 39 ] employed two neighbourhood strategies that help ABC algorithm in faster convergence towards finding high quality solutions.…”