Evolutionary many-objective optimization: A short review

By Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008

996 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/93b9c81f1c43acd0b79985b0e9d0d2f19cfe6159

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Citations in the corpus

Multi-Objective Parameter Selection for Classifiers (2012, background): “With four or more objectives, so-called many-objective optimization problems arise, which impose additional problems on the tuning process (see, e.g., Ishibuchi et al. 2008).”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “Besides, Ishibuchi et al. [49] proposed three metrics to measure convergence and diversity of solution sets in a many-objective context: maximum sum of the objective values (MaxSum), the sum of the maximum objective values (SumMax), and the sum of the range of the objective values of each objective…”
Performance metrics in multi-objective optimization (2015, background): “42 Spread: Generalized Spread [43] Δ∗ 43 Success Counting [44] SCC 44 Sum of maximum objective values [45] Smax 45 The adjusted Rand Index [46] 46 The average distance of points to the PF [47] 47 The delineation metric [48] 48 The diversity metric [48] 49 The front Spread [49] FS 50 The front to Set”
A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, background): “The performance deterioration can be largely attributed to the fact that the rate of nondominated solutions in a limited population increases exponentially as the number of objectives increases, making traditional Pareto-based MOEAs fail to distinguish these solutions [12], [13].”
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, methodology): “In this paper, we review some of the past efforts [9], [10], [11], [12], [13], [14] in devising many-objective EMOs and outline some viable directions for designing efficient many-objective EMO methodologies.”
Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014, background): “In addition to the deficiency in the definition of Pareto dominance stated above, other complications such as visualization of high-dimensional objective spaces [1], a large number of individuals needed to obtain a good representation of the Pareto front [1], and a very high computational cost [3], ”
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, background): “Fortunately, many-objective optimization, to solve problems with more than three objectives, has received increasing attention recently and many evolutionary algorithms have been developed for such problems [3, 6].”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “It is well known that Pareto dominancebased fitness evaluation mechanisms do not work well on many-objective problems [4], [72].”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, positive): “Nevertheless, recent proposals in the EMO field have managed to deal with a significantly large number of objectives inwhat is called evolutionarymany-objective optimization [98,137] and their adaptation to multiobjective GFS has become feasible.”
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, background): “However, according to the aforementioned second challenge, the active diversity promotion leads to a detrimental effect on the convergence toward the EF [13], [22], [23], due to the presence of dominance resistant solutions [24].”
Grasshopper optimization algorithm for multi-objective optimization problems (2017, background): “Since solving such problems is out of the scope of this work, interested readers are referred to a survey in [34].”
A Survey of Multi-Objective Sequential Decision-Making (2013, background): “However, over the last decade there has been growing interest in evolutionary methods for so-called many-objective problems, which have at least four and sometimes more than ﬁfty objectives (Ishibuchi, Tsukamoto, & Nojima, 2008).”