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首页> 外文期刊>Evolutionary computation >Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces
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Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces

机译：在组合和连续搜索空间中以景观分析为指导的约束处理

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摘要

The notion and characterisation of fitness landscapes has helped us understand the performance of heuristic algorithms on complex optimisation problems. Many practical problems, however, are constrained, and when significant areas of the search space are infeasible, researchers have intuitively resorted to a variety of constraint-handling techniques intended to help the algorithm manoeuvre through infeasible areas and toward feasible regions of better fitness. It is clear that providing constraint-related feedback to the algorithm to influence its choice of solutions overlays the violation landscape with the fitness landscape in unpredictable ways whose effects on the algorithm cannot be directly measured. In this work, we apply metrics of violation landscapes to continuous and combinatorial problems to characterise them. We relate this information to the relative performance of six well-known constraint-handling techniques to demonstrate how some properties of constrained landscapes favour particular constraint-handling approaches. For the problems with sampled feasible solutions, a bi-objective approach was the best performing approach overall, but other techniques performed better on problems with the most disjoint feasible areas. For the problems with no measurable feasibility, a feasibility ranking approach was the best performing approach overall, but other techniques performed better when the correlation between fitness values and the level of constraint violation was high.
机译：适应性景观的概念和特征已帮助我们了解启发式算法在复杂优化问题上的性能。但是，许多实际问题受到限制，并且当搜索空间的重要区域不可行时，研究人员会直观地诉诸于各种约束处理技术，旨在帮助算法在不可行区域内朝着更好适应性的可行区域进行机动。显然，向算法提供与约束相关的反馈以影响其对解决方案的选择，以不可预测的方式将违规态势与适应性态势叠加在一起，而其对算法的影响无法直接测量。在这项工作中，我们将违规态势的度量应用于连续和组合问题以表征它们。我们将此信息与六种众所周知的约束处理技术的相对性能相关联，以证明约束景观的某些属性如何偏向于特定的约束处理方法。对于抽样可行解的问题，双目标方法总体上是效果最好的方法，但是其他技术在可行区最不相交的问题上表现更好。对于没有可衡量的可行性的问题，可行性排序方法总体上是性能最好的方法，但是当适应度值和约束违反程度之间的相关性很高时，其他技术的效果会更好。

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