SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization

By Eckart Zitzler, Marco Laumanns, Lothar Thiele. 2002

2,243 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bdbff9a75074fee3401f17caa8839ddc0801cb31

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Citations in the corpus

A review of multiobjective test problems and a scalable test problem toolkit (2006, background): “…which extends Bentley and Wakeﬁeld’s test suite to include an integer valued nonseparable multiobjective problem with ﬂat regions and isolated optima, and a real-world problem; and a test suite by Zitzler et al. [11], which includes ZLT1, ZDT6, QV1, Kur1, and a binary encoded knapsack problem.”
A systematic approach to exploring embedded system architectures at multiple abstraction levels (2006, methodology): “To solve the above multiobjective optimization problem, we use the (improved) Strength Pareto Evolutionary Algorithm (SPEA2) [35] that finds a set of approximated Paretooptimal mapping solutions, i.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, unknown): “4) SPEA2+SDE applies a shift-based density estimation (SDE) strategy to the well-known SPEA2 [85].”
PISA: A Platform and Programming Language Independent Interface for Search Algorithms (2003, methodology): “In a second experiment, a large application written in Java was tested with the well known multiobjective optimizer SPEA2 [12] written in C++ using the library TEA [5].”
A Scalable Multi-objective Test Problem Toolkit (2005, background): “[10], and others [11, 12], they tend to be of limited scope, and are often ad hoc and consequently difficult to analyse (but by the same token, some also have unusual Pareto optimal geometries).”
A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization (2015, methodology): “Therefore, the commonly used dominance-based methods for multiobjective optimization, such as NSGA-II [2] or SPEA2 [3], do not offer satisfactory results.”
Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition (2014, background): “One of the major reasons could be that most popular MOEAs, such as NSGA-II [3], SPEA2 [4], and PAES [37], compare solutions’ qualities mainly based on their dominance relations.”
Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “Some popular examples of multiobjective evolutionary algorithms (MOEAs) commonly in use today include nondominated sorting genetic algorithm (NSGA-II) [11], NSGA-III [12], [13], SPEA2 [14], MOEA/D [15]–[17], and indicator-based MOEAs [18].”
The balance between proximity and diversity in multiobjective evolutionary algorithms (2003, methodology): “[37], the PAES (Pareto Archived Evolution Strategy) by Knowles and Corne [38], the M–PAES (Memetic PAES) by Knowles and Corne [39] and the MIDEA (Multi–objective Mixture–based Iterated Density Estimation Evolutionary Algorithm) by Thierens and Bosman [33], [7].”
Indicator-Based Selection in Multiobjective Search (2004, methodology): “(b) Performance comparison for adaptive IBEA, IBEAHD, SPEA2 and NSGA- II solving problems DTLZ6 (top) and EXPO2 (bottom).”
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, unknown): “I. Introduction VOLUTIONARY multiobjective optimization (EMO) methodologies”
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, background): “In order to evolve the population towards the optimum as well as diversify the individuals uniformly along the obtained trade-off surface, the fitness of individuals should contain he information in terms of both convergence and diversity.”