SPEA2: Improving the strength pareto evolutionary algorithm

By Eckart Zitzler, Marco Laumanns, Lothar Thiele. 2001

6,264 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b13724cb54ae4171916f3f969d304b9e9752a57f

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Citations in the corpus

Novelty-Based Multiobjectivization (2011, background): “The diversity on the Pareto front can be maintained using several ways such as fitness sharing [7], crowding [5] or clustering methods [25].”
Multi-objective models for lot-sizing with supplier selection (2011, background): “Most of these variations refer to MOO problems (Augusto et al., 2006; Zitzler et al., 2001; Srinivas and Deb, 1994) and focus on the goals of MOO, resulting in a number of powerful MOO algorithms, such as NSGA-II and SPEA2, which are evolved versions of NSGA and SPEA, respectively (for more…”
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I (2014, methodology): “The most representative elitist MOEAs include strength Pareto evolutionary algorithm (SPEA) [11] and SPEA2 [12], Pareto archived evolutionary strategy (PAES) [13], Pareto envelope-based selection algorithm (PESA) [14] and PESA-II [15], and nondominated sorting genetic algorithm-II (NSGA-II) [16].”
Multi-objective Architecture Search for CNNs (2018, background): “A plethora of evolutionary algorithms for multi-objective optimization has been proposed (e.g., Deb et al. (2002); Zitzler et al. (2001)).”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “The Pareto-based MOEAs selected for comparison were NSGA-II [24], SPEA2 [110], and (cid:4) -MOEA [67]; aggregation approaches were the MSOPS and the Repeated Single Objective (RSO) [41]; while indicator-based methods were two variants of the Indicator-based Evolutionary Algorithms (IBEA) [106] and…”
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”
Preference-based Pareto optimization in certain and noisy environments (2009, neutral): “In addition to the NSGA-II algorithm, also the Strength Pareto Evolutionary Algorithm 2 (SPEA2) (Zitzler et al. 2001) and Multiobjective Particle Swarm Optimization (MOPSO) (Coello Coello et al. 2004) have been used.”
A tutorial on multiobjective optimization: fundamentals and evolutionary methods (2018, methodology): “There is a multiple of introductory articles that preceded this tutorial: • In Zitzler et al. (2004) a tutorial on state-of-the-art evolutionary computation methods in 2004 is provided including Strength Pareto Evolutionary Algorithm Version 2 (SPEA2) (Zitzler et al. 2001), Non-dominated Sorting Gen”
SMPSO: A new PSO-based metaheuristic for multi-objective optimization (2009, methodology): “The selected algorithms are: two genetic algorithms, NSGA-II [3] and SPEA2 [17]; a scatter search approach, AbYSS [13]; a cellular genetic algorithm, MOCell [12], and OMOPSO [14].”
A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP (2007, methodology): “in [48], was developed as an improvement of SPEA [49].”
Contemporary Symbolic Regression Methods and their Relative Performance (2021, background): “[48] Eckart Zitzler, Marco Laumanns, and Lothar Thiele.”
The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “Besides NSGA-II, jMetal includes the implementation of a number of both classic and modern multi-objective optimizers; some examples are: SPEA2 [27], PAES [26], OMOPSO [29], MOCell [30], AbYSS [31], MOEA/D [32], GDE3 [33], IBEA [34], or SMPSO [35] .”
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization (2009, methodology): “In order to examine the effectiveness of COEA, a comparative study including COEA, CCEA [56], SPEA2 [69], and NSGAII [11] is carried out based upon the three benchmark problems listed in Table II.”