Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures
Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures
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DOI：
10.1109/cec.2006.1688438
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
H. Ishibuchi;Y. Nojima;Tsutomu Doi
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文献类型：
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作者：
H. Ishibuchi;Y. Nojima;Tsutomu Doi
我们比较了单目标遗传算法（SOGA）和多目标遗传算法（MOGA）在多目标背包问题中的应用。首先，我们讨论比较 SOGA 的单个解决方案与 MOGA 的解决方案集的困难。我们还讨论了将多次运行 SOGA 的多个解决方案与单次运行 MOGA 的大量解决方案进行比较的困难。结果表明，现有的绩效衡量标准不一定适合这种比较。然后我们通过多目标背包问题的计算实验将SOGA 与MOGA 进行比较。双目标问题的实验结果表明，即使根据 SOGA 中使用的标量适应度函数对 MOGA 进行评估，MOGA 的性能也优于 SOGA。这是因为 MOGA 更有可能逃离局部最优。另一方面，四目标问题的实验结果表明，MOGA 的搜索能力随着目标数量的增加而降低。最后，我们提出了一个混合算法框架，其中 SOGA 中的标量适应度函数在 MOGA 中概率性地使用，以提高 Pareto 前沿解的收敛性。
We compare single-objective genetic algorithms (SOGAs) with multi-objective genetic algorithms (MOGAs) in their applications to multi-objective knapsack problems. First we discuss difficulties in comparing a single solution by SOGAs with a solution set by MOGAs. We also discuss difficulties in comparing several solutions from multiple runs of SOGAs with a large number of solutions from a single run of MOGAs. It is shown that existing performance measures are not necessarily suitable for such comparison. Then we compare SOGAs with MOGAs through computational experiments on multi-objective knapsack problems. Experimental results on two-objective problems show that MOGAs outperform SOGAs even when they are evaluated with respect to a scalar fitness function used in SOGAs. This is because MOGAs are more likely to escape from local optima. On the other hand, experimental results on four-objective problems show that the search ability of MOGAs is degraded by the increase in the number of objectives. Finally we suggest a framework of hybrid algorithms where a scalar fitness function in SOGAs is probabilistically used in MOGAs to improve the convergence of solutions to the Pareto front.