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SPEA specifications


Unique identifier OMICS_33129
Alternative name Strength Pareto Evolutionary Algorithm
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Windows
Programming languages C
Computer skills Advanced
Version 2
Stability Stable
Source code URL https://sop.tik.ee.ethz.ch/pisa/selectors/spea2/spea2_c_source.tar.gz
Maintained Yes




No version available


  • person_outline Eckart Zitzler
  • person_outline Marco Laumanns

Additional information


Publications for Strength Pareto Evolutionary Algorithm

SPEA citations


AMOBH: Adaptive Multiobjective Black Hole Algorithm

Comput Intell Neurosci
PMCID: 5733773
PMID: 29348741
DOI: 10.1155/2017/6153951

[…] suitable for solving MOPs. The most popular multiobjective evolutionary algorithms (MOEAs) are Pareto dominance based algorithms [], such as nondominated sorting genetic algorithm II (NSGA-II) [] and strength Pareto evolutionary algorithm II (SPEA-II) []. Besides the criterion of Pareto dominance, they also adopted a diversity related secondary criterion to promote a good distribution of the solut […]


Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi Objective Evolutionary Algorithms

Front Hum Neurosci
PMCID: 5435763
PMID: 28572762
DOI: 10.3389/fnhum.2017.00261

[…] al length of sleep spindles in the MASS SS2 datasets ranged from only 8 to 30 min per night, we decided to extract a subsample of 60 min from each whole night sleep in the training datasets to reduce SPEA2 computation time. Considering that our tested detectors used intrinsically different methods to estimate frequency, we did not want to create a common, single subsample for all of them. Instead, […]


The improved business valuation model for RFID company based on the community mining method

PLoS One
PMCID: 5411069
PMID: 28459815
DOI: 10.1371/journal.pone.0175872

[…] st) samples were obtained. We scaled the collecting data into interval [, ].We compared the performance of our method with several existing methods: RMs, support vector machine (SVM), NNs, NSGAII and SPEA2, which are two highly competitive algorithms for bi-objective optimization. We adopted 4 most commonly used RMs: multiple curvilinear regression with the kernel function of y = axb (RM1), y=1a+b […]


Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

PLoS One
PMCID: 5305220
PMID: 28192508
DOI: 10.1371/journal.pone.0172033

[…] the externally stored elitists.The diversity of the elitists can be promoted using techniques such as adaptive grid adopted in Pareto archived evolution strategy (PAES) [] and PAES2 [], clustering in strength Pareto evolutionary algorithm (SPEA) [], crowding distance in NSGA-II [], fitness sharing in niched Pareto genetic algorithm (NPGA) [], maximin sorting in [], M-nearest-neighbors product-base […]


Optimization of Allelic Combinations Controlling Parameters of a Peach Quality Model

Front Plant Sci
PMCID: 5167719
PMID: 28066450
DOI: 10.3389/fpls.2016.01873

[…] ctives (elements of the Pareto optimal set). Many MOEAs have been suggested over the last few decades. The most studied and the best performing variations among the MOEAs are the PESA (), PESA-II (), SPEA (), SPEA-II (), NSGA (), and NSGA-II () algorithms. The latter (Non-dominated Sorting Genetic Algorithm-II) is currently considered as the reference algorithm in the MOEAs community since it has […]


An Encoding Technique for Multiobjective Evolutionary Algorithms Applied to Power Distribution System Reconfiguration

Sci World J
PMCID: 4225859
PMID: 25401144
DOI: 10.1155/2014/506769

[…] In this case, the problem consists in simultaneously minimizing power losses, f 1, and the number of operated switches, f 3. At the end of the multiobjective optimization process, the EAs based on SPEA2 and NSGA-II algorithms must provide a set of solutions which describe a near Pareto-optimal front for the DSR problem. The different solutions obtained are shown in .From , it can be observed th […]


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SPEA institution(s)
Computer Engineering and Networks Laboratory (TIK), ETH Zentrum, Zurich, Switzerland; Department of Electrical Engineering Swiss Federal Institute of Technology (ETH) Zurich, ETH Zentrum, Zurich, Switzerland

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