A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems
By Xiufen Zou, Yu Chen, Minzhong Liu, Lishan Kang. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2008
317 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/5c557cda7008adc933b9be9649a4c89ca92756e2
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Citations in the corpus
- A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “The -dominance [25], CDAS-dominance [26], α-dominance [27], fuzzy Pareto dominance [28], L-dominance [29], θ -dominance [30], and so on [31]–[33] have been experimentally verified to be more effective than the original Pareto dominance relation when searching toward the optimum.”
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, background): “…[23], [24] studied the fuzzification of the Pareto-dominance relation to design the ranking scheme to select promising solutions; the L -optimality paradigm was proposed in [25] to pick up solutions whose objectives were with the same importance by considering their objective value improvements.”
- Many-Objective Evolutionary Algorithms (2015, methodology): “From Theorems 1 and 2 in Zou et al. [2008], we can show that Pareto dominance implies LD, but the other way around is not true.”
- An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, background): “In addition, some nonPareto-based approaches, such as average rank [37], L-optimality [38], and rank-dominance [39], have also demonstrated their abilities for handling problems with a large number of objectives.”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “The third category adopts fuzzy logic to define new dominance relations, such as (1−k)-dominance [28], L-dominance [29], and fuzzy dominance [11].”
- A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, background): “To enhance the convergence pressure, the most intuitive idea is to directly modify the definition of traditional Pareto dominance, such as ε-dominance [12], [13], L-optimality [14], fuzzy dominance [15], preference order ranking [16], and θ -dominance [17].”
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “Moreover, according to the practice in [7] and [19], the solutions that do not dominate reference point are discarded for the HV calculation.”
- A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, unknown): “This type of ideas has been widely adopted for solving MaOPs, such as -dominance [22], [23], L-optimality [24], fuzzy dominance [25], and preference order ranking [26].”
- An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, background): “The first step is to choose the N members from the combined parent and offspring t t t preserve elite members of the parent”
- A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, background): “Examples of modified dominance definitions include -dominance [17], [18], L-optimality [19], preference order ranking [20], and fuzzy dominance [21].”
- A survey on multi-objective evolutionary algorithms for many-objective problems (2014, unknown): “…i ng R e l a ti v e e n t r opy [ 19 ] , c ov e r a g e [ 108 ] [ 112 ] D TLZ ( 1 - 2 , 6 ) [ 25 ] 3 , 4 , 5 , 6 , 7 , 8 , 9 D yn a m i c M O E A [ 112 ] , I B E A [ 106 ] , M S O PS [ 40 ] , M S O PS -II [ 43 ] , N S GA -II [ 24 ] C onv e r g e n ce [ 26 ] , H yp e r vo l u m e [ 1 08 ] [ 70 ] M…”
- A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “Following the practice in [2] and [22], the points that do not dominate reference point are discarded for the HV calculation.”
- A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization (2015, methodology): “nondominance-based methods such as -dominance [8], griddominance [32], dominance using preferred goals [33] and use of secondary ranking schemes for diversity manage-”
- A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, unknown): “On the other hand, some non-Pareto-based techniques, such as average rank [49], ranking dominance [45], favour relation [64], winning score [51], and some distancebased rank methods [20], [53], [69], [75], have also been found to be promising in converging towards the optimum.”