Opposition-Inspired Strategies for Tabu Search approaches proposed for Knapsack Problems

The family of Knapsack Problems (KP) has been relevant in many works and studies as their use in modeling, simplifying complex problems or decision-making processes. Because of its importance, several metaheuristic algorithms have been designed or evaluated using this type of problem. In some variants of the KP, Tabu Search approaches are competitive or part of the state-of-the-art. This work proposes opposition-inspired strategies to improve the diversification of Tabu Search (TS) algorithms proposed for solving KPs. We use the well-known TSTS algorithm to evaluate our strategies, designed for solving the Multidemand Multidimensional Knapsack Problem. Results show that the usage of our opposite strategies allow the target algorithm to improve its performance in several benchmark instances.