A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
By Yuan Yuan, Hua Xu, Bo D. Wang, Xin Yao. IEEE Transactions on Evolutionary Computation, 2016
666 citations recorded by the source corpus.
Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the nondominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is evaluated on a number of well-known benchmark problems having 3-15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test functions considered in this paper, and it is compared favorably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/011090f0fec8157acabb91dfff8dc5a0dd496981
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Citations in the corpus
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “However, such kind of algorithms now still perform unsatisfactorily on MaOPs [17].”
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, methodology): “First, it has been reported in [11], [15], [70], and [71] that the HV result is largely affected by the nadir points of the problem to be optimized.”
- An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “As analyzed in [58], better performance of NSGA-III can be obtained by using the source code implemented by the authors of [54] than the jMetal 5.0 source code [59].”
- Localized Weighted Sum Method for Many-Objective Optimization (2018, methodology): “based on decomposition (MOEA/D) [17] and its variants MOEA/D-DD [18], MOEA/D-DU [19], NSGA-III [20], and θ -DEA [21].”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “There exist many techniques for developing new dominance relations in the literature, such as expanding the dominance area [6], [7], gridding the objective space [8], [9], adopting the fuzzy logic [10], [11], and defining the dominance relation by weight vectors [12], [13].”
- PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “ guided evolutionary algorithm RVEA* [52] 2016 RVEA embedded with the reference vector regeneration strategy SPEA/R [53] 2016 Strength Pareto evolutionary algorithm based on reference direction θ-DEA [54] 2016 θ-dominance based evolutionary algorithm Multi-Objective Genetic Algorithms for Large-Scal”
- A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “, the intercepts along each objective axis, which is based on the solution of a system of linear equations [29].”
- 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 [13], [14], L-optimality [15], fuzzy dominance [16], preference order ranking [17] and θdominance [18].”
- Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017, methodology): “5, we show the reference points used for the IGD calculation in [29] and a set of obtained solutions by a single run of MOEA/D-PBI with = 5 (IGD is 5.”