Evolutionary computation for large-scale multi-objective optimization: A decade of progresses
Large-scale multi-objective optimization problems (MOPs) that involve a large number of
decision variables, have emerged from many real-world applications. While evolutionary …
decision variables, have emerged from many real-world applications. While evolutionary …
Bio-inspired computation: Where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
A correlation-guided layered prediction approach for evolutionary dynamic multiobjective optimization
When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …
algorithms, the historical moving directions of some special points along the Pareto front …
Evolutionary multiobjective optimization: open research areas and some challenges lying ahead
CA Coello Coello, S González Brambila… - Complex & Intelligent …, 2020 - Springer
Evolutionary multiobjective optimization has been a research area since the mid-1980s, and
has experienced a very significant activity in the last 20 years. However, and in spite of the …
has experienced a very significant activity in the last 20 years. However, and in spite of the …
A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization
The performance of traditional multiobjective evolutionary algorithms (MOEAs) often
deteriorates rapidly as the number of decision variables increases. While some efforts were …
deteriorates rapidly as the number of decision variables increases. While some efforts were …
Large-scale multiobjective optimization via reformulated decision variable analysis
With the rising number of large-scale multiobjective optimization problems (LSMOPs) from
academia and industries, some multiobjective evolutionary algorithms (MOEAs) with …
academia and industries, some multiobjective evolutionary algorithms (MOEAs) with …
Methods that optimize multi-objective problems: A survey and experimental evaluation
K Taha - IEEE Access, 2020 - ieeexplore.ieee.org
Most current multi-optimization survey papers classify methods into broad objective
categories and do not draw clear boundaries between the specific techniques employed by …
categories and do not draw clear boundaries between the specific techniques employed by …
Evolutionary search with multiview prediction for dynamic multiobjective optimization
Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective
optimization problem which varies over time. As changes in DMOP may exist some patterns …
optimization problem which varies over time. As changes in DMOP may exist some patterns …
A simulation-optimization model for sustainable product design and efficient end-of-life management based on individual producer responsibility
This paper integrates two decision problems, namely, the design alternative selection and
EOL option determination, for a family of products based on individual producer …
EOL option determination, for a family of products based on individual producer …
Intersection signal timing optimization: A multi-objective evolutionary algorithm
The rapid motorization of cities has led to the increasingly serious contradiction between
supply and demand of road resources, and intersections have become the main bottleneck …
supply and demand of road resources, and intersections have become the main bottleneck …