Many-Objective Optimization: An Engineering Design Perspective

By Peter John Fleming, Robin Charles Purshouse, Robert J. Lygoe. International Conference on Evolutionary Multi-Criterion Optimization, 2005

407 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/0bd08d04ba57a3465cc41ea279dcc6dc17d3057b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Mechanical Engineering

Citations in the corpus

Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, background): “IN the real world, many problems [1], [2] can be regarded as many-objective optimization problems (ManyOPs) [3], [4], a category of multiobjective optimization problems (MOPs) [5] with more than three objectives.”
Dynamic behavior and vibration control of a tensegrity structure (2010, background): “Incorporating preferences is also considered to help in handling conflicting objectives (Fleming et al. 2005).”
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “MaOPs exist widely in real-world applications [1], such as engineering design [3], water distribution system design [4], airfoil”
Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, unknown): “the EMO community [12], [24], [39], [57].”
Pareto Multi-Task Learning (2019, methodology): “By formulating the MTL with many tasks as a multi-objective optimization problem, we get a many-objective optimization problem, which is indeed a popular research topic in the multi-objective optimization community [45–48].”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “Farina and Amato (2004), Fleming et al. (2005), and Purshouse and Fleming (2007) observe that the proportion of locally Pareto non-dominated solutions tends to become large as the number of objectives increases.”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “Among the aforementioned alternatives, the parallel-coordinates plot [44] may be considered the most applied visualization technique in many-objective problem solving research using MOEAs [34,68,89].”
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, background): “Digital Object Identifier 10.1109/TEVC.2012.2227145 I N THE REAL world, it is not uncommon to face a problemwith several objectives to be met simultaneously, which is known as a multiobjective optimization problem (MOP).”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “design [1], air traffic control [2], groundwater monitoring [3], and molecular design [4].”
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, background): “, control system design [3], [4], industrial scheduling [5], [6], and software engineering [7], [8].”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “At the other end of the spectrum, there is an emerging paradigm for “many” objective analysis of more than four objectives (Fleming et al., 2005; Woodruff et al., 2013).”
Evolutionary many-objective optimization: A short review (2008, methodology): “, the exponential increase in the number of non-dominated solutions that are necessary for the approximation of the Pareto front) has often been tackled by incorporating preference information in EMO algorithms [23]-[25].”
Many-Objective Evolutionary Algorithms (2015, unknown): “As an important class of MOPs, MaOPs appear widely in many real-world applications, such as engineering design [Fleming et al. 2005], air trafﬁc control 32(660) 31(758) 26(422) 54(420) Note : We conducted a comprehensive literature survey using the following databases: Google Scholar, IEEE Xplore…”
A Survey of Multi-Objective Sequential Decision-Making (2013, background): “Fleming et al. (2005) note that one of the most eﬀective approaches used in many-objective evolutionary computation is to incorporate user preferences to restrict the search space to a small region of interest.”