面向复杂约束优化问题的进化算法综述

约束优化是多数实际工程应用优化问题的呈现方式.进化算法由于其高效的表现,近年来被广泛应用于约束优化问题求解.但约束条件使得问题解空间离散、缩小、改变,给进化算法求解约束优化问题带来极大挑战.在此背景下,融合约束处理技术的进化算法成为研究热点.此外,随着研究的深入,近年来约束处理技术在复杂工程应用问题优化中得到了广泛发展,例如多目标、高维、等式优化等.根据复杂性的缘由,将面向复杂约束优化问题的进化优化分为面向复杂目标的进化约束优化算法和面向复杂约束场景的进化算法两种类别进行综述,其中,重点探讨了实际工程应用的复杂性对约束处理技术的挑战和目前研究的最新进展,并最后总结了未来的研究趋势与挑战.

Overview of Evolutionary Algorithms for Complex Constrained Optimization Problems

Most of engineering optimization problems can be formulated as constrained optimization problems. Evolutionary algorithms have been widely used in optimization constrained problems in recent years due to their sound performance. Nevertheless, the constraints make the solution space of the problem discrete, shrink and change, which bring great challenges to the evolutionary algorithm to solve the constrained optimization problem. The evolutionary algorithm integrating constraint handling technology has become a research hotspot. In addition, constraint processing techniques have been widely developed in the optimization of complex engineering application problems with the deepening of research in recent years, such as multi-objective, high-dimensional, equality constraint, etc. This study divides the evolutionary optimization for complex constraint optimization problems into evolutionary optimization algorithms for complex objectives and evolutionary algorithms for complex constraint scenarios according to the complexity. The challenges of constraint handling technology due to the complexity of practical engineering applications and the latest research progress in current research are discussed. Finally, the future research trends and challenges are summarized.