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International Conference on Power Electronics and Intelligent Transportation System

Quantum computing-based Ant Colony Optimization algorithm for TSP

Xiaoming You, Xing-wai Miao, Sheng Liu

Published Dec 1, 2009• 15 citations
Publisher

Abstract

A novel self-adaptive Ant Colony Optimization algorithm based on Quantum mechanism for Traveling salesman problem(TQACO) is proposed. Firstly, initializing the population of the ant colony with superposition of Q-bit, Secondly, using self-adaptive operator, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. This mechanism offers the ability to escape from local optima and can self-regulate the production of diverse antibodies. Because of the quantum superposition and rotation it can maintain quite nicely the population diversity than the classical evolutionary algorithm, because of the self-adaptive operator it can obtain more optimal solution and the solution quality is improved significantly. TSP benchmark instances Chn144 results demonstrate the superiority of TQACO in this paper.

DOI
10.1109/peits.2009.5406879

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A novel self-adaptive Ant Colony Optimization algorithm based on Quantum mechanism for Traveling salesman problem(TQACO) is proposed. Firstly, initializing the population of the ant colony with superposition of Q-bit, Secondly, using self-adaptive operator, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. This mechanism offers the ability to escape from local optima and can self-regulate the production of diverse antibodies. Because of the quantum superposition and rotation it can maintain quite nicely the population diversity than the classical evolutionary algorithm, because of the self-adaptive operator it can obtain more optimal solution and the solution quality is improved significantly. TSP benchmark instances Chn144 results demonstrate the superiority of TQACO in this paper.
A novel Ant Colony Optimization algorithm based on Quantum mechanism for Multi-objective traveling salesman problem (MQACO) is proposed. To improve algorithm performance we use self-adaptive operator, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. We analyze the technology to improve algorithm performance. Self-adaptive algorithm has advantages in terms of the adaptability; reliability and the learning ability over traditional organizing algorithm. TSP benchmark instances Chn144 results demonstrate the superiority of MQACO by different parameter in this paper.
The quantum ant colony algorithm (QACO) is explored as a solution to the traveling salesman problem (TSP), targeting inefficiencies such as slow convergence and local optima entrapment found in traditional ant colony optimization (ACO) methods. By integrating quantum computing elements, specifically quantum rotation gates and qubits, into the ACO framework, QACO demonstrates enhanced efficiency and faster convergence. This paper delves into the foundational principles of quantum computing and their application to refine ACO, effectively solving the TSP. An in-depth analysis is provided, showcasing QACO's advantages over traditional ACO through empirical examples. These examples illustrate QACO's potential to improve path planning for applications within the rapidly growing e-commerce and logistics sectors. The integration of quantum mechanisms facilitates a more dynamic pheromone update process, which significantly reduces the likelihood of premature convergence and enhances solution quality in complex optimization scenarios. This advancement indicates a promising direction for future research in algorithmic optimization in both theoretical and practical applications.
Aiming at the Traveling Salesman Problem based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate,a hybrid quantum ant colony optimization algorithm is presented.In this algorithm,the pheromone on each path is encoded by a group of quantum bits,the quantum rotation gate and ant's tour are used to update the pheromone so as to accelerate its convergence speed.To avoid the search falling into local optimum,the new neighborhood exchange strategy is designed to improve solution efficiency.Some cases from the TSP library(TSPLIB)are used to experiment,the results show that the algorithm has rapider convergence speed and higher accuracy than the classical ant colony algorithm.
Aiming at the disadvantage of optimization problems based on traditional ant colony optimization (ACO)which easily falls into local optimums and has worse performance in convergence,the paper unifies the quantum evolutionary algorithm (QEA) and the ant colony algorithm,in this algorithm,pheromone is represented by quantum bits and updated by a new quantum rotation gate adjustment srrategy.And this paper applies separately this algorithm and the traditional ACO to traveling salesman problem(TSP),The simulation results show that the algorithm of this paper has excellent global optimization properties and quicker convergence than the traditional ACO.
The algorithm is based on the combination of quantum evolutionary algorithm(QEA) and ant colony system(ACS),a new algorithm,quantum ant colony algorithm(QACA) is proposed.The core is that Q-bit and quantum rotation gate adopted in QEA are introduced into ACS to represent and update the pheromone respectively,so it has better diversity and global search capacity.The ex-perimental result demonstrates that QACA can get better solutions to some traveling salesman problems(TSP) than the solutions given in existing bibliographer,it indicate that the algorithm is effective to solve TSP.
Ant Colony Algorithm(ACA) demonstrates the superiority in solving TSP,and Quantum Evolution Algorithm (QEA) has the performance of high efficiency on combinational optimization problems,so combining the thought of ACA with QEA,a novel quantum ant colony algorithm for TSP is proposed.In this algorithm,the pheromone on each path is encoded by a group of quantum bits,and a new pheromone representation is designed,called quantum pheromone.The quantum rotation gate and the best tour are applied to update the pheromone so as to accelerate its convergence speed.To avoid the search falling into local optimum,the strategy of quantum crossover is designed to improve the information structure of population.Simulation results show that the algorithm has fast convergence speed and global optimal ability,and the algorithm is more effective than ACS.