Abstract
0-1 knapsack problem is a classical Non-deterministic Polynomial Hard (NP-Hard) problem in combinatorial optimization. It has a wide range of applications in real life, such as the distribution of goods in logistics companies, capital calculation, storage, and distribution, etc. This paper studies the adoption of three meta-heuristic approaches for solving the 0–1 knapsack problem. A novel quantum-inspired Tabu Search (QTS) that combines a classical Tabu Search (TS) and Quantum-inspired Evolutionary Algorithm (QEA), Ant Colony swarm intelligence Algorithm (ACO), and Genetic Algorithm (GA) with augmented fitness function. Based on empirical analysis of computer simulations and comparative demonstrations, we show that applying such metaheuristic results in high efficiency in terms of the quality of obtained solutions, computational time, and robustness when dealing with the underlying 0–1 knapsack optimization problem.
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