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改进混合蛙跳算法求解旅行商问题
罗雪晖,杨烨,李霞
(深圳大学信息工程学院,广东深圳 518060)
摘要:以旅行商问题(TSP)为例,引入调整序思想设计了局部搜索策略,同时在全局信息交换过程中加入变异操作,提出一种改进混合蛙跳算法求解TSP问题。实验结果表明,与遗传算法和粒子群优化算法相比较,改进混合蛙跳算法在求解TSP问题上具有更好的搜索性能和顽健性。
关键词:混合蛙跳算法;旅行商问题;局部搜索;全局信息交换
中图分类号:TP18 文献标识码:A 文章编号:1000-436X(2009)07-0130-06
Modified shuffled frog-leaping algorithm to
solve traveling salesman problem
LUO Xue-hui, YANG Ye, LI Xia
(College of Information Engineering, Shenzhen University, Shenzhen 518060,China)
Abstract: Modified shuffled frog-leaping algorithm to solve TSP was proposed, which presented the concept of adjustment sequence to design the strategy of local searching, and added the mutation operation in the global exchange of information. Experimental results indicate that, compared with ic algorithm and particle swarm optimization algorithm, the proposed algorithm has more powerful search capability and more strong robustness in solving TSP.
Key words: shuffled frog-leaping algorithm; traveling salesman problem; local search; global information exchange
1 引言
收稿日期:2008-08-02;修回日期:2008-11-20
基金项目:国家自然科学基金资助项目(60772148)
Foundation Item: The National Natural Science Foundation of China (600772148)
混合蛙跳算法是2000年由Muzaffar Eusuff和Kevin Lansey提出的一种基于群智能的亚启发式计算优化算法,用于解决离散组合优化问题[1]。作为一种新型的仿生物学智能优化算法,SFLA结合了基于模因(meme)进化的模因演算法(MA,memetic algorithm)和基于群体行为的粒子群算法(PSO, particle swarm optimization)2种群智能优化算法的优点。该算法具有概念简单,调整的参数少,计算速度快,全
罗雪晖,杨烨,李霞
(深圳大学信息工程学院,广东深圳 518060)
摘要:以旅行商问题(TSP)为例,引入调整序思想设计了局部搜索策略,同时在全局信息交换过程中加入变异操作,提出一种改进混合蛙跳算法求解TSP问题。实验结果表明,与遗传算法和粒子群优化算法相比较,改进混合蛙跳算法在求解TSP问题上具有更好的搜索性能和顽健性。
关键词:混合蛙跳算法;旅行商问题;局部搜索;全局信息交换
中图分类号:TP18 文献标识码:A 文章编号:1000-436X(2009)07-0130-06
Modified shuffled frog-leaping algorithm to
solve traveling salesman problem
LUO Xue-hui, YANG Ye, LI Xia
(College of Information Engineering, Shenzhen University, Shenzhen 518060,China)
Abstract: Modified shuffled frog-leaping algorithm to solve TSP was proposed, which presented the concept of adjustment sequence to design the strategy of local searching, and added the mutation operation in the global exchange of information. Experimental results indicate that, compared with ic algorithm and particle swarm optimization algorithm, the proposed algorithm has more powerful search capability and more strong robustness in solving TSP.
Key words: shuffled frog-leaping algorithm; traveling salesman problem; local search; global information exchange
1 引言
收稿日期:2008-08-02;修回日期:2008-11-20
基金项目:国家自然科学基金资助项目(60772148)
Foundation Item: The National Natural Science Foundation of China (600772148)
混合蛙跳算法是2000年由Muzaffar Eusuff和Kevin Lansey提出的一种基于群智能的亚启发式计算优化算法,用于解决离散组合优化问题[1]。作为一种新型的仿生物学智能优化算法,SFLA结合了基于模因(meme)进化的模因演算法(MA,memetic algorithm)和基于群体行为的粒子群算法(PSO, particle swarm optimization)2种群智能优化算法的优点。该算法具有概念简单,调整的参数少,计算速度快,全
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