Since dozens years ago, various metaheuristic methods, such as genetic algorithm, antcolony algorithms, have been successfully applied to combinational optimization problem. However, as one of the members, ITO algori...Since dozens years ago, various metaheuristic methods, such as genetic algorithm, antcolony algorithms, have been successfully applied to combinational optimization problem. However, as one of the members, ITO algorithm has only been employed in continuous optimization, it needs further design for combinational optimization problem.In this paper, a discrete ITO algorithm inspired by ITO stochastic process is proposedfor travelling salesman problems (TSPs). Some key operators, such as move operator,wave operator, are redesigned to adapt to combinational optimization. Moreover, theperformance of ITO algorithm in different parameter selections and the maintenance ofpopulation diversity information are also studied. By combining local search methods(such as 2-opt and LK-opt) with ITO algorithm, our computational results of the TSPproblems show that ITO algorithm is currently one of the best-performing algorithmsfor these problems.展开更多
基金The authors thank the anonymous reviewers for providing valuable comments to improve this paper,and also thank for the financial support by the NSF of China under Grant Nos.61170305 and 60873114the State Scholarship Fund(File No.2012084291)of CSC and NSF of USA under Grant CMMI-1162482.
文摘Since dozens years ago, various metaheuristic methods, such as genetic algorithm, antcolony algorithms, have been successfully applied to combinational optimization problem. However, as one of the members, ITO algorithm has only been employed in continuous optimization, it needs further design for combinational optimization problem.In this paper, a discrete ITO algorithm inspired by ITO stochastic process is proposedfor travelling salesman problems (TSPs). Some key operators, such as move operator,wave operator, are redesigned to adapt to combinational optimization. Moreover, theperformance of ITO algorithm in different parameter selections and the maintenance ofpopulation diversity information are also studied. By combining local search methods(such as 2-opt and LK-opt) with ITO algorithm, our computational results of the TSPproblems show that ITO algorithm is currently one of the best-performing algorithmsfor these problems.
