Most real-world problems involve some type of optimization problems that are often
constrained. Numerous researchers have investigated several techniques to deal with …

Handling constrained multiobjective optimization problems (CMOPs) is extremely
challenging, since multiple conflicting objectives subject to various constraints require to be …

Constrained multi-objective optimization problems (CMOPs) are widespread in practical
applications such as engineering design, resource allocation, and scheduling optimization …

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter
optimization algorithms in current use. DE operates through similar computational steps as …

In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA),
is presented for solving constrained multi-objective problems. The MOWCA is based on …

This article develops two new algorithms for constrained expensive black-box optimization
that use radial basis function surrogates for the objective and constraint functions. These …

This research presents a systematic and integrated approach that extends the correlated
storage assignment strategy to improve the efficiency of warehouse order picking …

In this paper, a new multiobjective optimization framework based on nondominated sorting
and local search (NSLS) is introduced. The NSLS is based on iterations. At each iteration …

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive
differential evolution, named ADE-MOIA, in which the introduction of differential evolution …

Dynamic constrained multiobjective optimization involves irregular changes in the
distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused …