Multiobjective evolutionary algorithms: A survey of the state of the art

By Aimin Zhou, B. Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, Qingfu Zhang. Swarm and Evolutionary Computation, 2011

2,213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/6275daa3f29c2e2a750a6687f3651e4d1b412c58

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Citations in the corpus

Recent Advances in Bayesian Optimization (2022, background): “A subsequent work is the extension of the output-space-entropy based AF in the context of MOPs, known as MESMO [14].”
Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, methodology): “Most of the existing EMO algorithms belong to this group, and among them, several representative algorithms, such as the nondominated sorting genetic algorithm II (NSGA-II) [13], strength Pareto EA 2 (SPEA2) [81], and Pareto envelope-based selection algorithm II (PESA-II) [11], are being widely appl”
An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, background): “To approximate the Pareto optimal set, a number of multi-objective evolutionary algorithms (MOEAs) have been developed during the past two decades, which can be roughly classified into three categories [1], [2].”
Evolutionary neural AutoML for deep learning (2019, methodology): “In the multiobjective version of CoDeepNEAT, the ranking is computed from successive Pareto fronts [17, 54] generated from the primary and secondary objectives.”
A comprehensive review of firefly algorithms (2013, background): “The contribution of FA to multi-objective optimization [99] is as follows.”
Grasshopper Optimisation Algorithm: Theory and application (2017, background): “Due to their simplicity, gradient-free mechanism, high local optima avoidance, and considering problems as black boxes, nature-inspired algorithms have been applied widely in science and industry [34-36].”
Grasshopper optimization algorithm for multi-objective optimization problems (2017, background): “Multi-objective optimization using heuristics has recently attracted much attention and is the main focus of this work [23].”
Multi-objective Architecture Search for CNNs (2018, background): “Evolutionary multi-objective optimization (Abraham & Goldberg, 2005; Zhou et al., 2011) is a subfield of multiobjective optimization.”
Bio-inspired computation: Where we stand and what's next (2019, background): “This section may provide the reader with a general overview of bio-inspired computation, but the literature focused on this field is immense, with thorough bibliographic compendiums already contributed in the form of exhaustive surveys [42, 43, 44, 45].”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “In spite of the large number of MOEAs in the literature [22], t here often exist some difficulties in applying and using these algorithms since the source code of most algo rithms had not been provided by the authors.”
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, background): “Evolutionary algorithms (EAs), which are population-base d, are well suited for solving MOPs and MaOPs as they can obtain a solution set in one single run [5].”
Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, background): “…algorithms (CMOEAs) are a promising way to deal with CMOPs [10], little effort has been devoted to developing CMOEAs in the community of evolutionary computation although both multiobjective EAs (MOEAs) [11] and constraint-handling techniques [12] have been extensively investigated individually.”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, background): “Since, an evolutionary algorithm (EA) because of its population based nature, can obtain multiple P-O solutions in a single simulation run, multiobjective EAs (MOEAs) have been very popular in solving MOPs [3], [4].”
A Review of Random Search Methods (2015, background): “[84]) and use it to solve an aircraft spare parts allocation problem, and Lee et al.”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, background): “Thanks to this attractive property, multiobje ctive evolutionary algorithms (MOEAs) have witnessed a boom of development over the past two decades [1].”
A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “These algorithms have been shown to be effective in solving MOPs with two or three objectives [10].”