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Sustainable scheduling of distributed flow shop group: A collaborative multi-objective evolutionary algorithm driven by indicators
Sustainable scheduling within the manufacturing field has garnered substantial attention
from both academia and industry. The escalating market demands have heightened …
from both academia and industry. The escalating market demands have heightened …
Multiple populations for multiple objectives framework with bias sorting for many-objective optimization
The convergence and diversity enhancement of multiobjective evolutionary algorithms
(MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active …
(MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active …
Multi-objective optimization problem with hardly dominated boundaries: Benchmark, analysis, and indicator-based algorithm
The hardly dominated boundary (HDB) is commonly observed in multi-objective optimization
problems (HDBMOPs). However, there are only a few benchmark problems related to HDB …
problems (HDBMOPs). However, there are only a few benchmark problems related to HDB …
Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive
In practice, the multi-objective optimization problem (MOP) is typically challenging in two
aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other …
aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other …
Hypervolume-based cooperative coevolution with two reference points for multi-objective optimization
An important issue in hypervolume-based evolutionary multiobjective optimization (EMO)
algorithms is the specification of a reference point for hypervolume calculation. However, its …
algorithms is the specification of a reference point for hypervolume calculation. However, its …
Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design
Dynamic tariffs play an important role in demand response, contributing to smoothing power
consumption and reducing generation capacity requirement and carbon emission. However …
consumption and reducing generation capacity requirement and carbon emission. However …
Moboa: a proposal for multiple objective bean optimization algorithm
L **e, X Lu, H Liu, Y Hu, X Zhang, S Yang - Complex & Intelligent Systems, 2024 - Springer
The primary objective of multi-objective evolutionary algorithms (MOEAs) is to find a set of
evenly distributed nondominated solutions that approximate the Pareto front (PF) of a multi …
evenly distributed nondominated solutions that approximate the Pareto front (PF) of a multi …
Handling objective preference and variable uncertainty in evolutionary multi-objective optimization
Evolutionary algorithms (EAs) are widely employed in multi-objective optimization (MOO) to
find a well-distributed set of near-Pareto solutions. Among various types of practicalities that …
find a well-distributed set of near-Pareto solutions. Among various types of practicalities that …
Multi-objective evolutionary algorithm with evolutionary-status-driven environmental selection
The hardly dominated boundary (HDB) is a common feature of multi-objective optimization
problems (MOPs). Previous studies have proposed several multi-objective evolutionary …
problems (MOPs). Previous studies have proposed several multi-objective evolutionary …
Selection Strategy Based on Proper Pareto Optimality in Evolutionary Multi-objective Optimization
On the multi-objective optimization problems (MOP), the dominance-resistant solution (DRS)
refers to the solution that has inferior objective values but is difficult to dominate by other …
refers to the solution that has inferior objective values but is difficult to dominate by other …