Contemporary industrial equipment is increasingly developing towards complexity. In order
to ensure the high reliability and sustainability of industrial equipment, more flexible …

In evolutionary multiobjective optimization, maintaining a good balance between
convergence and diversity is particularly crucial to the performance of the evolutionary …

When solving constrained multiobjective optimization problems, an important issue is how to
balance convergence, diversity, and feasibility simultaneously. To address this issue, this …

Achieving balance between convergence and diversity is a key issue in evolutionary
multiobjective optimization. Most existing methodologies, which have demonstrated their …

The interests in multiobjective and many-objective optimization have been rapidly
increasing in the evolutionary computation community. However, most studies on …

To approximate the Pareto front, most existing multiobjective evolutionary algorithms store
the nondominated solutions found so far in the population or in an external archive during …

Adaptive operator selection (AOS) is used to determine the application rates of different
operators in an online manner based on their recent performances within an optimization …

Abstract Dynamic Multi-objective Optimization Problem (DMOP) is emerging in recent years
as a major real-world optimization problem receiving considerable attention. Tracking the …

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a
multiobjective optimization problem into a set of scalar optimization subproblems and …

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-
dependent objective functions, while the ones with a changing number of objectives have …