PESA-II: region-based selection in evolutionary multiobjective optimization

By David Wolfe Corne, Nick R. Jerram, Joshua D. Knowles, Martin John Oates. 2001

1,102 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a1a0a95fb3a304be4fea2701e7d8bccb3a6fbf17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ye Tian
2. Yaochu Jin
3. Xing-yi Zhang
4. Ran Cheng
5. Satyasai Jagannath Nanda
6. Yuan Yuan
7. Yanan Sun
8. Yi Xiang

Citations in the corpus

• An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “Among the compared algorithms, MOEA/D, NSGAII, PESA-II and IBEA are four classical MOEAs for solving MOPs, where MOEA/D is a decomposition based MOEA, NSGA-II and PESA-II are Pareto dominance based MOEAs and IBEA is an indicator based MOEA; MOEA/DD, NSGA-III and RVEA are three popular decomposition ”
• MOEA/D with Adaptive Weight Adjustment (2014, methodology): “Typical examples include the strength Pareto evolutionary algorithm (SPEA; Zitzler and Thiele, 1999) and its improved version SPEA2 (Zitzler et al., 2001), the Pareto-envelope-based selection algorithm (PESA; Knowles and Corne, 2000) and its enhanced version PESA-II (Corne et al., 2001), the 232 Evo”
• jMetal: A Java framework for multi-objective optimization (2011, background): “Implementation of a number of modern multi-objective optimization algorithms: NSGA-II [6], SPEA2 [8], PAES [7], PESA-II [13], OMOPSO [14], MOCell [15], AbYSS [16], MOEA/D [17], Densea [18], CellDE [19], GDE3 [20], FastPGA [21], IBEA [22], SMPSO [23], MOCHC [24], and SMS-EMOA [25].”
• Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “This fact has led to the considerable growth in multi-objective evolutionary algorithms (MOEA), from VEGA in [27], to the most recent techniques like NSGA-II [10], SPEA-2 [37] and PESA-II [8].”
• An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “The first type is non-dominated-based methods, and representative examples include NSGA-II (Deb et al (2002)), PAES-II (Corne et al (2001)), SPEA-II (Zitzler et al (2001)), NSGA-III (Deb and Jain (2014)) and so on.”
• An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization (2015, methodology): “algorithms have been developed to tackle MOPs, for example, Pareto envelop-based selection algorithm II (PESA-II) [1],”
• A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “Focused on the secondary selection criterion for Pareto-based algorithms, Li et al. [23] introduced a shiftbased density estimation (SDE) strategy and applied it to three popular Pareto-based algorithms, i.e., NSGA-II, SPEA2, and PESAII, forming three new algorithms (i.e., NSGA-II+SDE, SPEA2+SDE, an”
• Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, methodology): “In this section, we apply SDE to three classical Pareto-based EMO algorithms: NSGA-II [13], SPEA2 [81], and PESA-II [11].”
• On the best design for undulatory swimming (2013, methodology): “Principle The evolutionary algorithm used in the present study is adapted from the PESA-II method of Corne et al. (2001).”
• One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, unknown): “Some of the most popular algorithms, according to the state-ofthe-art review of Coello Coello (2005), are the Strength Pareto Evolutionary Algorithm (SPEA; Zitzler & Thiele, 1999) and its successor SPEA II (Zitzler et al., 2001), the Pareto Archive Evolution Strategy (PAES; Knowles & Corne, 2000), t”
• Multi-objective optimization using genetic algorithms: A tutorial (2006, methodology): “PESA-II [15] follows a more direct approach, namely region-based selection, where cells but not individual solutions are selected during the selection process.”
• An Evolutionary Approach to Multiobjective Clustering (2007, methodology): “One example of a MOEA, the Pareto envelope-based selection algorithm version 2 (PESA-II) [39], forms the basis of MOCK’s clustering phase.”
• A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, background): “As a result, several popular elitism MOEAs have been developed, including the elitist non-dominated sorti ng genetic algorithm (NSGA-II) [14], the strength Pareto evolutionary algorithm [15], [16] and the Pareto envelope-bas ed selection algorithm [17], [18], among many others.”