In this article, we discuss a novel method for solving large-scale nonlinear optimization problems using particle swarm optimization (PSOC) techniques. The approach is designed to handle complex problems with multiple constraints and nonlinear terms, which are often challenging to solve using traditional optimization methods.
To address these challenges, the authors propose a hybrid algorithm that combines PSOC with a special option set for IPOPT, a popular optimization software. This enables the algorithm to efficiently search the solution space and converge to the optimal solution. The authors also provide a detailed analysis of the algorithm’s performance, demonstrating its effectiveness in solving large-scale optimization problems.
One of the key innovations of the proposed method is the use of a "little trick" that reduces the number of collision avoidance constraints. This allows the algorithm to focus more on the main optimization problem, leading to faster convergence and better solution quality. The authors also show that their approach can handle problems with very low cardinality of active points, making it particularly useful for solving real-world problems with complex constraints.
The article provides a comprehensive overview of the proposed method and its applications, as well as a detailed analysis of its performance. The authors demonstrate the effectiveness of their approach through extensive numerical experiments and compare it to other state-of-the-art optimization methods. Overall, the paper makes an important contribution to the field of optimization by providing a powerful new tool for solving large-scale nonlinear optimization problems.
National Science Foundation of China Grant Supported Research: A New Optimization Algorithm for Solving Large-Scale Nonlinear Programming Problems
