A Differential Constrained Optimization Algorithm Based on Component Model

The resolution and implementation of constrained optimization problems consistently remain a prominent subject in the realm of contemporary complex engineering applications. In this paper, the amalgamation of extraction and partitioning technology with the fundamental differential evolution algorithm is achieved, facilitating the transplantation of feasible component information through the algorithm’s inherent mutation operation and the segmentation technology’s feasible component breeding pool. Simultaneously, the adroit integration of adaptive constraint processing technology occurs within the original selection operation. In this paper, a component-based differential constraint algorithm is proposed and substantiated through numerical experiments conducted on common test functions. The experimental findings showcase the algorithm’s comparable or superior performance in terms of effectiveness, robustness, accuracy, and ease of implementation when compared to certain existing algorithms.