Many-objective optimization with dynamic constraint handling for constrained optimization problems

• Published in: Soft computing (Berlin, Germany) Vol. 21; no. 24; pp. 7435 - 7445
• Main Authors: Li, Xi, Zeng, Sanyou, Li, Changhe, Ma, Jiantao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Constraints; Control; Engineering; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Handling; Hierarchies; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Multiple objective analysis; Objectives; Optimization; Optimization algorithms; Optimization techniques; Robotics; Many-objective optimization; Dynamic constraint optimization; Reference-point-based nondominated sorting; Constrained optimization
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-016-2286-8

In real-world applications, the optimization problems are usually subject to various constraints. To solve constrained optimization problems (COPs), this paper presents a new methodology, which incorporates a dynamic constraint handling mechanism into many-objective evolutionary optimization. Firstly we convert a COP into a dynamic constrained many-objective optimization problem (DCMaOP), which is equivalent to the COP, then the proposed many-objective optimization evolutionary algorithm with dynamic constraint handling, called MaDC, is realized to solve the DCMaOP. MaDC uses the differential evolution (DE) to generate individuals, and a reference-point-based nondominated sorting approach to select individuals. The effectiveness of MaDC is verified on 22 test instances. The experimental results show that MaDC is competitive to several state-of-the-art algorithms, and it has better global search ability than its peer algorithms.