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Binary Particle Swarm Optimization Based Hyper-Heuristic for Solving the Set-Union Knapsack Problem
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作者 CHEN Xiang LUO Jinyan LIN Geng 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2021年第4期305-314,共10页
The set-union knapsack problem(SUKP)is proved to be a strongly NP-hard problem,and it is an extension of the classic NP-hard problem:the 0-1 knapsack problem(KP).Solving the SUKP through exact approaches is computatio... The set-union knapsack problem(SUKP)is proved to be a strongly NP-hard problem,and it is an extension of the classic NP-hard problem:the 0-1 knapsack problem(KP).Solving the SUKP through exact approaches is computationally expensive.Therefore,several swarm intelligent algorithms have been proposed in order to solve the SUKP.Hyper-heuristics have received notable attention by researchers in recent years,and they are successfully applied to solve the combinatorial optimization problems.In this article,we propose a binary particle swarm optimization(BPSO)based hyper-heuristic for solving the SUKP,in which the BPSO is employed as a search methodology.The proposed approach has been evaluated on three sets of SUKP instances.The results are compared with 6 approaches:BABC,EMS,gPSO,DHJaya,b WSA,and HBPSO/TS,and demonstrate that the proposed approach for the SUKP outperforms other approaches. 展开更多
关键词 set-union knapsack problem binary programming HYPER-HEURISTICS particle swarm optimization
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改进的教与学优化算法求解集合联盟背包问题 被引量:4
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作者 吴聪聪 贺毅朝 赵建立 《计算机科学与探索》 CSCD 北大核心 2018年第12期2007-2020,共14页
针对集合联盟背包问题(set-union knapsack problem,SUKP)难以使用确定性算法求解的情况,提出了一种快速求解SUKP问题的改进二进制教与学优化算法(modified binary teaching-learning-based optimization,MBTLBO)。首先,给出了教与学优... 针对集合联盟背包问题(set-union knapsack problem,SUKP)难以使用确定性算法求解的情况,提出了一种快速求解SUKP问题的改进二进制教与学优化算法(modified binary teaching-learning-based optimization,MBTLBO)。首先,给出了教与学优化算法的二进制编码方法;然后,针对求解SUKP问题中的候选解,提出改进的修复优化策略(modified SUKP greedy repairing and optimization algorithm,MS-GROA)。该策略增加了修复后可行解的二次优化,从而提升了对SUKP问题的求解精度。另外为了克服教与学优化算法易早熟,求解精度低,后期收敛速度慢等弱点,在"教"阶段和"学"阶段引入差分算法的交叉算子,通过平衡算法的开发能力和勘探能力,避免算法过早陷入局部极值;在精英个体周围按正态分布进行自适应局部搜索,提高算法的收敛速度和求解精度。三类SUKP实例测试表明,MBTLBO算法具有较高的求解精度和更快的收敛速度,是有效求解SUKP问题的方法。 展开更多
关键词 集合联盟背包问题(sukp) 教与学优化算法(TLBO) 二进制编码 修复和优化策略 正态分布
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