Abstract Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive problems. However, relatively little work been dedicated addressing constrained multi-objective discrete problems although there are many such the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed this paper for kind of problem. Specifically, random forest m...

Striking a balance between objective optimization and constraint satisfaction is essential for solving constrained multi-objective problems (CMOPs). Nevertheless, most existing evolutionary algorithms face significant challenges on CMOPs with intricate infeasible regions. To tackle these challenges, this paper proposes an adaptive two-population algorithm, named ATEA, which dynamically exploits...

Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using classical optimization methods, this paper presents a Multi-Objective Bees Algorithm (MOBA) for solving the multi-objective optimal of mechanical engineering problems design. In the pre...

In this paper, a globally convergent Newton-type proximal gradient method is developed for composite multi-objective optimization problems where each objective function can be represented as the sum of smooth and nonsmooth function. The proposed deals with unconstrained convex problems. This free from any kind priori chosen parameters or ordering information functions. At every iteration method...