The workflow scheduling problem considered difficult in the Cloud becomes even more
challenging when multiple scheduling criteria are used for optimization. It is much harder to …

The unprecedented outbreak of COVID-19 has left many multinational enterprises facing
extremely severe supply disruptions. Besides considering triple-bottom-line requirements …

Traditional multiobjective immune algorithms (MOIAs) widely use the domination
relationship and crowding distance metric to run the cloning operator, which places more …

Selection mechanisms based on performance indicators are popular for solving multi-
objective optimization problems (MOPs), which can provide a comprehensive evaluation of …

It is well known that domain knowledge helps design efficient problem solvers. The
regularity model based multiobjective estimation of distribution algorithm (RM-MEDA) is …

Many multimodal multiobjective optimization algorithms aim to find as many Pareto optimal
solutions as possible while the performance in the objective space is despised. More …

To enhance the segmentation performance and optimization efficiency of multi-objective
evolutionary clustering algorithms for noisy images, this study presents a knee point driven …

Various existing multimodal multi-objective evolutionary algorithms (MMEAs) efficiently
search for an approximation to the Pareto optimal front (PF), which consists of multiple …

In practice, the decision-maker (DM) may be only interested in a particular part of Pareto
optimal front (PF). For this reason, many preference-based multi-objective evolutionary …

In the field of preference-based evolutionary multiobjective optimization, optimization
algorithms are required to search for the Pareto optimal solutions preferred by the decision …