Multi-objective optimization problems are difficult to solve in that the optimized objectives are
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …

A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

This study introduces the extension of currently developed Seagull Optimization Algorithm
(SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull …

This paper proposes a data-driven two-stage multi-criteria decision-making (MCDM)
framework to investigate the optimal configuration of a stand-alone wind/PV/hydrogen …

This paper presents a multi-objective version of the artificial vultures optimization algorithm
(AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA) …

The remanufacturing process, driven by human–robot collaboration technology, is becoming
an important carrier for the circular economy, contributing to economic development while …

The traditional distributed flexible job shop scheduling problem (DFJSP) assumes that
operations of a job cannot be transferred between different factories. However, in real-world …

This study introduces the evolutionary multi-objective version of seagull optimization
algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm …

Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform
poorly when tackling many-objective optimization problems (MaOPs). This is mainly …

This paper proposes a multi-objective version of recently developed Spotted Hyena
Optimizer (SHO) called Multi-objective Spotted Hyena Optimizer (MOSHO). It is used to …