Pascoletti‐Serafini Method to Improve the Balance of Convergence Diversity and Feasibility in Constrained Optimization

Balancing convergence, diversity and feasibility remains a fundamental challenge in constrained optimization. This paper proposes MOEA/D‐PS, which introduces the Pascoletti‐Serafini (PS) scalarization framework into constrained single‐objective optimization for the first time. The algorithm reformulates constrained single‐objective problems into a bi‐objective optimization by treating constraint violation as a secondary objective, then integrates two synergistic mechanisms to achieve a satisfactory balance of convergence, diversity and feasibility: (1) direction vectors sampled from a normal distribution to guide the population search around the elite solution while preserving exploration capability; (2) an adaptive truncation parameter that dynamically adjusts the search region based on the current elite solution. Unlike standard multi‐objective transformations that waste resources approximating the entire Pareto front, the PS‐guided mechanism geometrically restricts exploration to the trajectory toward the feasible optimum, achieving a unified balance among convergence, diversity and feasibility. Comprehensive experiments on 24 benchmark functions from the CEC 2006 test suite show that MOEA/D‐PS attains the best overall ranking among seven state‐of‐the‐art algorithms.