IEEE Trans Cybern. 2015 Jan;45(1):40-52. doi: 10.1109/TCYB.2014.2317693. Epub 2014 May 7.
We propose a new archive-based steady-state micro genetic algorithm (ASMiGA). In this context, a new archive maintenance strategy is proposed, which maintains a set of nondominated solutions in the archive unless the archive size falls below a minimum allowable size. It makes the archive size adaptive and dynamic. We have proposed a new environmental selection strategy and a new mating selection strategy. The environmental selection strategy reduces the exploration in less probable objective spaces. The mating selection increases searching in more probable search regions by enhancing the exploitation of existing solutions. A new crossover strategy DE-3 is proposed here. ASMiGA is compared with five well-known multiobjective optimization algorithms of different types-generational evolutionary algorithms (SPEA2 and NSGA-II), archive-based hybrid scatter search, decomposition-based evolutionary approach, and archive-based micro genetic algorithm. For comparison purposes, four performance measures (HV, GD, IGD, and GS) are used on 33 test problems, of which seven problems are constrained. The proposed algorithm outperforms the other five algorithms.
我们提出了一种新的基于存档的稳态微遗传算法（ASMiGA）。在这种情况下，我们提出了一种新的存档维护策略，除非存档大小降至最小允许大小以下，否则该策略将在存档中维护一组非支配解。这使得存档大小具有适应性和动态性。我们还提出了一种新的环境选择策略和一种新的交配选择策略。环境选择策略减少了在可能性较小的目标空间中的探索。交配选择通过增强对现有解决方案的利用来增加在更可能的搜索区域中的搜索。这里提出了一种新的交叉策略 DE-3。ASMiGA 与五种不同类型的知名多目标优化算法（SPEA2 和 NSGA-II）、基于存档的混合离散搜索、基于分解的进化方法和基于存档的微遗传算法进行了比较。为了进行比较，在 33 个测试问题（其中 7 个问题受到约束）上使用了四个性能度量（HV、GD、IGD 和 GS）。所提出的算法优于其他五种算法。
