Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

Constrained multiobjective optimization problems (CMOPs) are challenging because of the
difficulty in handling both multiple objectives and constraints. While some evolutionary …

Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

Both objective optimization and constraint satisfaction are crucial for solving constrained
multiobjective optimization problems, but the existing evolutionary algorithms encounter …

Sparse multiobjective optimization problems (MOPs) have become increasingly important in
many applications in recent years, eg, the search for lightweight deep neural networks and …

In the last two decades, a variety of different types of multiobjective optimization problems
(MOPs) have been extensively investigated in the evolutionary computation community …

There exist many multiobjective optimization problems (MOPs) containing a large number of
decision variables in real-world applications, which are known as large-scale MOPs. Due to …

This paper presents a multi-objective version of the artificial vultures optimization algorithm
(AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA) …

This paper describes jMetalPy, an object-oriented Python-based framework for multi-
objective optimization with metaheuristic techniques. Building upon our experiences with the …