A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation

Bai, Hui; Zheng, Jinhua; Yu, Guo; Yang, Shengxiang; Zou, Juan

doi:10.1016/j.ins.2018.10.027

Cited by 26 publications

(6 citation statements)

References 43 publications

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“…The Pareto approach was successfully applied in solving multi-optimization problems (MOP) using both local (Jaszkiewicz 2018) and global optimization algorithms (Bai et al 2019, Mustaffa et al 2019) in recent research.…”

Section: Joint Inversionmentioning

confidence: 99%

An application of the NSGA-II algorithm in Pareto joint inversion of 2D magnetic and gravity data

Miernik

Węglińska

Danek

et al. 2021

geol

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Joint inversion is a widely used geophysical method that allows model parameters to be obtained from the observed data. Pareto inversion results are a set of solutions that include the Pareto front, which consists of non-dominated solutions. All solutions from the Pareto front are considered the most feasible models from which a particular one can be chosen as the final solution. In this paper, it is shown that models represented by points on the Pareto front do not reflect the shape of the real model. In this contribution, a collective approach is proposed to interpret the geometry of models retrieved in inversion. Instead of choosing single solutions from the Pareto front, all obtained solutions were combined in one “heat map”, which is a plot representing the frequency of points belonging to all returned objects from the solution set. The conducted experiment showed that this approach limits the problem of equivalence and is a promising way of representing the geometry of the model that was retrieved in the inversion process.

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Section: Joint Inversionmentioning

confidence: 99%

An application of the NSGA-II algorithm in Pareto joint inversion of 2D magnetic and gravity data

Miernik

Węglińska

Danek

et al. 2021

geol

View full text Add to dashboard Cite

show abstract

“…VaEA uses maximum-vector-angle-first principle and worseelimination principle to guarantee the uniformity of solution set. In [23], a Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation (SPSAT) to enhance convergence and diversity. The volume dominance strategy is to differentiate the advantages and disadvantages by comparing the volume size of the individual and the reference point in [24].…”

Section: In Addition Moeas Can Easily Solve Complex Optimizationmentioning

confidence: 99%

A Novel Nonlinear Expanded Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization Problems

Wei

Liu

2021

IEEE Access

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Multi-objective optimization problems exist widely in scientific research and engineering applications. With the number of objectives increasing, the proportion of non-dominated individuals in the population of many-objective optimization problems increases sharply, resulting in a reduction of convergence pressure of the traditional multi-objective optimization algorithms. In some cases, the optimal solutions may be located in the special regions, such as many discrete regions and the regions with very few feasible solutions. In this case, the existing nonlinear expanded evolutionary algorithm can not find the true Pareto fronts. To address the limitation, a novel nonlinear expanded dominance relation based manyobjective evolutionary algorithm is proposed to handle many-objective optimization problems. Experimental results show that compared with the state of art algorithms, the proposed algorithm is effective for DTLZs, in terms of IGD, PD and GD metrics.

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“…The objective space partition-based multi-objective evolutionary optimization [20], [23], [39], [40] is one competitive branch of decomposition-based approaches [41]. The algorithms in this branch employ a set of reference vectors to partition the objective space of an MOP into a series of connected and disjoint subspaces, and a reference vector corresponds to a subspace.…”

Section: A Main Processmentioning

confidence: 99%

Fine-Grained Ensemble of Evolutionary Operators for Objective Space Partition Based Multi-Objective Optimization

Hong

Jiang

2021

IEEE Access

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A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation

Cited by 26 publications

References 43 publications

An application of the NSGA-II algorithm in Pareto joint inversion of 2D magnetic and gravity data

An application of the NSGA-II algorithm in Pareto joint inversion of 2D magnetic and gravity data

A Novel Nonlinear Expanded Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization Problems

Fine-Grained Ensemble of Evolutionary Operators for Objective Space Partition Based Multi-Objective Optimization

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