约束优化问题的模因生存演化
Memetic Viability Evolution for Constrained Optimization
摘要：
当约束条件限制了搜索空间的可行区域时，进化算法的性能会受到严重损害。例如，虽然协方差矩阵自适应演化策略是无约束优化问题的最有效的算法之一，它不能很容易地应用于约束的。在这里，我们使用的概念，从模因计算，即多个单位的算法信息的和谐组合，和可行性进化，人工进化的另一种抽象，设计一种新的方法来解决不等式约束的优化问题。可行性演化强调消除不满足可行性标准的解决方案，定义为目标和约束的边界。这些边界在搜索过程中进行调整，以基于协方差矩阵自适应演化策略驱动局部搜索单元的种群朝向可行区域。这些单元可以通过差分进化算子进行重组。至关重要的性能，我们的方法，自适应调度器之间切换的剥削和探索，通过选择提前本地搜索单元和/或重组它们。所提出的算法可以优于几个国家的最先进的方法对不同的基准和工程问题，无论是解决方案的质量和所需的计算资源。
英文摘要：
The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from Memetic Computing, i.e. the harmonious combination of multiple units of algorithmic information, and Viability Evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability Evolution emphasizes elimination of solutions not satisfying viability criteria, defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on Covariance Matrix Adaptation Evolution Strategy, towards feasible regions. These units can be recombined by means of Differential Evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
