EDA++: Estimation of Distribution Algorithms With Feasibility Conserving Mechanisms for Constrained Continuous Optimization
提出一种改进的分布估计算法EDA++，通过种子初始化、学习和映射等机制处理非线性约束，保证最终解的可行性，并在基准问题上验证了其速度、鲁棒性和效率。
Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this article, we adopt the framework of estimation of distribution algorithms (EDAs) and propose a new algorithm (EDA++) equipped with some mechanisms to deal with nonlinear constraints. These mechanisms are associated with different stages of the EDA, including seeding, learning, and mapping. It is shown that, besides increasing the quality of the solutions in terms of objective values, the feasibility of the final solutions is guaranteed if an initial population of feasible solutions is seeded to the algorithm. The EDA with the proposed mechanisms is applied to two suites of benchmark problems for constrained continuous optimization and its performance is compared with some state-of-the-art algorithms and constraint-handling methods. Conducted experiments confirm the speed, robustness, and efficiency of the proposed algorithm in tackling various problems with linear and nonlinear constraints.