Along with the developments of numerous MaOO algorithms in the last decades, comparing
the performance of MaOO algorithms with one another is also highly needed. Many studies …

Existing multiobjective evolutionary algorithms (MOEAs) perform well on multiobjective
optimization problems (MOPs) with regular Pareto fronts in which the Pareto optimal …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …

The worker flexibility and green production related factors are two key aspects that widely
exit in real-life production and seriously affect the production efficiency and ecological …

Rapid development of evolutionary algor ithms in handling many-objective optimization
problems requires viable methods of visualizing a high-dimensional solution set. The …

Ensuring water, energy, and food security with minimum damage to groundwater resources
is a key challenge to achieve sustainable development in arid areas. To address this …

In evolutionary computation, balancing the diversity and convergence of the population for
multiobjective evolutionary algorithms (MOEAs) is one of the most challenging topics …

Many-objective problems refer to the optimization problems containing more than three
conflicting objectives. To obtain a representative set of well-distributed non-dominated …

Researches have shown difficulties in obtaining proximity while maintaining diversity for
many-objective optimization problems. Complexities of the true Pareto front pose challenges …