When tackling constrained multi-objective optimization problem, evolutionary algorithms
grapple with the simultaneous need to optimize the conflict objectives and satisfy …

This paper proposes a two-phase evolutionary algorithm framework for solving multi-
objective optimization problems (MOPs), which allows different users to flexibly handle …

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

Constrained multiobjective optimization problems (CMOPs) are widespread in real-world
applications. Nevertheless, CMOPs with discontinuous feasible regions are challenging for …

The paper presents a method of incorporating decision maker preferences into multi-
objective meta-heuristics. It is based on tradeoff coefficients and extends their applicability …

In the constrained multi-objective optimization problems, the pursuit of feasibility could
improve convergence but will lead to the loss of diversity. For optimization algorithm …

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

The core task of constrained multiobjective optimization is to achieve a tradeoff between
exploration and exploitation as well as a tradeoff between constraints and objectives. We …

In evolutionary multi-objective optimization, it is crucial for the evolutionary algorithm to
maintain a good balance between convergence and diversity. The recently proposed …

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) proposed in
2007 has shown to be effective in solving multiobjective optimization problems. However …