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Soft Computing

A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

Hua-Pei Chiang, Yao–Hsin Chou, Chia-Hui Chiu, Shu–Yu Kuo, Yueh‐Min Huang

Published Dec 26, 2013• 74 citations
Publisher
DOI
10.1007/s00500-013-1203-7

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In this study, we learned more about quantum properties from quantum and metaheuristic algorithms after conducting many experiments. We then propose an improved version of Quantum-inspired Tabu Search (QTS), which en-hances the utilization of population information. The new version is called “Amplitude-Ensemble” QTS (AE-QTS). This makes AE-QTS more similar to the real quantum search algorithm, Grover Search Algorithm, in abstract concept, while keeping the simplicity of the algorithm. Later, we demonstrate the AE-QTS on the classical combinatorial optimization 0/1 knapsack problem. Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average increase of nearly 20% in all cases and even by 30% in some cases. Even as the problem complexity increases, the quality of the solutions found by our method remains superior to that of the QTS. These results confirm that our method has better search performance.
Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to highdependence problems, they experience difficulty in finding the global optimum. To address this problem, this paper proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its Q-bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependence problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum not gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependence problems and accelerate the optimization process by using entangled states. This paper uses nine benchmark functions to test the search ability of the entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency.
Reversible logic plays an important role in quantum computation, which is a promising research field. The reversible logic synthesis problem focuses on generating a reversible circuit automatically and finding the lowest cost when an output function is given. The synthesis of reversible logic circuits can be formulated as a combinatorial optimization problem. This paper proposes a new evolutionary algorithm for synthesizing reversible circuits based on Quantum-Inspired Tabu Search Algorithm (QTS). The proposed algorithm uses the QTS-based approach to find fewer gates and reduce the cost of reversible circuits. This method is simpler, has better performance in computational cost, and reduce the gate counts of reversible circuits. This paper also compares experimental results with other heuristic and evolutionary algorithms. The final outcome shows that the QTS-based approach performs much better than other algorithms.