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An Empirical Comparison of Several Recent Multi-objective Evolutionary Algorithms [chapter]

Thomas White, Shan He
2012 IFIP Advances in Information and Communication Technology  
In the past years, several novel Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed.  ...  This paper, for the first time, presents the results of an exhaustive performance comparison of an assortment of 5 new and popular algorithms on the DTLZ benchmark functions using a set of well-known performance  ...  In recent years, Multi-Objective Evolutionary Algorithms (MOEAs) have been attracting more attention due to their superior performance over traditional multi-objective optimisation algorithms, in terms  ... 
doi:10.1007/978-3-642-33409-2_6 fatcat:ivt6r5mz2jd3tb6vtzlpkimqiy

On Statistical Analysis of MOEAs with Multiple Performance Indicators [article]

Hao Wang and Carlos Igncio Hernández Castellanos and Tome Eftimov
2020 arXiv   pre-print
Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs and aim to determine a proper ranking thereof.  ...  This performance analysis's effectiveness is supported by an experimentation conducted on four algorithms, 16 problems, and 6 different numbers of objectives.  ...  Comparing multi-objective optimization algorithms using an ensemble of quality indicators with deep statistical comparison approach.  ... 
arXiv:2012.00886v1 fatcat:a2spikh3cfce3pnntyiwctq4ze

A Homogeneous Distributed Computing Framework for Multi-objective Evolutionary Algorithm [chapter]

Ki-Baek Lee, Jong-Hwan Kim
2013 Advances in Intelligent Systems and Computing  
To implement this framework into an evolutionary algorithm, the evolutionary process of multi-objective particle swarm optimization (MOPSO) is employed.  ...  This paper proposes a homogeneous distributed computing (HDC) framework for multi-objective evolutionary algorithm (MOEA).  ...  Introduction Recently in most of real world problems, the importance of multi-objective evolutionary algorithm (MOEA) has been in the limelight.  ... 
doi:10.1007/978-3-642-37374-9_65 fatcat:fpyottcls5acnp5vu4feomoukq

A multi-depot location routing problem to reduce the differences between the vehicles' traveled distances; a comparative study of heuristics

Hengameh Hadian, Amir-Mohammad Golmohammadi, Akbar Hemmati, Omolbanin Mashkani
2019 Uncertain Supply Chain Management  
To solve the problem, a Multi-Objective Imperialist Competitive Algorithm (MOICA) is developed.  ...  Based on response surface methodology, for each algorithm, several crossover and mutation strategies are adjusted.  ...  Comparative Meta-Heuristic (NSGA II) Here, we endeavor to evaluate and demonstrate the efficiency of our proposed MOICA by comparison with a famous multi-objective evolutionary algorithms, named NSGA-II  ... 
doi:10.5267/j.uscm.2018.6.001 fatcat:sy6yeb75o5dtjogvzhdejcddhi

Evolutionary Algorithms for Solving Unconstrained, Constrained and Multi-objective Noisy Combinatorial Optimisation Problems [article]

Aishwaryaprajna, Jonathan E. Rowe
2021 arXiv   pre-print
We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments.  ...  We compare several adaptations of UMDA for multi-objective problems with the Simple Evolutionary Multi-objective Optimiser (SEMO) and NSGA-II.  ...  Noisy Combinatorial Multi-Objective Problems In this section, we empirically examine the performances of several evolutionary algorithms on noisy combinatorial multi-objective problems.  ... 
arXiv:2110.02288v1 fatcat:vzy3vfq34nfxlbji7hyiboglki

Speeding up many-objective optimization by Monte Carlo approximations

Karl Bringmann, Tobias Friedrich, Christian Igel, Thomas Voß
2013 Artificial Intelligence  
Turning theory into practice, we employ these results in the ranking procedure of the multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) as an example of a state-of-the-art method  ...  Many state-of-the-art evolutionary vector optimization algorithms compute the contributing hypervolume for ranking candidate solutions.  ...  We applied the algorithms to several classes of benchmark functions that are scalable to an arbitrary number of objectives m.  ... 
doi:10.1016/j.artint.2013.08.001 fatcat:klfbnun72jhmfdxzdezzmkubsq

Recent trends in metaheuristics for stochastic combinatorial optimization

Walter Gutjahr
2011 Open Computer Science  
Special attention is given to multi-objective SCO as well as to combinations of metaheuristics with mathematical programming.  ...  AbstractThis short overview is an addendum to a recent literature survey by Bianchi et al. on metaheuristics for stochastic combinatorial optimization (SCO).  ...  Most frequently, techniques for multi-objective SCO in this class adopt modifications of evolutionary algorithms. Let us give some examples.  ... 
doi:10.2478/s13537-011-0003-3 fatcat:cuze6wblbrgltf37s32qyvti4m


Ray S. Chen, Jeffrey K. Hollingsworth
2015 Proceedings of the 5th International Workshop on Runtime and Operating Systems for Supercomputers - ROSS '15  
Evolutionary algorithms for multi-objective optimization attempt to quickly and accurately discover the set of Pareto-optimal solutions, or the Pareto frontier.  ...  We demonstrate the quality of ANGEL using a multi-objective benchmark test suite.  ...  As an alternative, evolutionary algorithms can be used to solve the multi-objective optimization problem directly.  ... 
doi:10.1145/2768405.2768409 dblp:conf/hpdc/ChenH15 fatcat:34l2irlkwjad3hvjq6sw3thqpi

Big Models for Big Data using Multi objective averaged one dependence estimators [article]

Mrutyunjaya Panda
2016 arXiv   pre-print
In this paper, multi-objective evolutionary algorithm ENORA is used to select the features in a multi-class classification problem.  ...  Even though, many researchers tried to explore the various possibilities on multi objective feature selection, still it is yet to be explored with best of its capabilities in data mining applications rather  ...  The authors provided an empirical comparison of various MOO algorithms, considering six test functions, which are aimed at giving an impression to understand which technique performs well under what condition  ... 
arXiv:1610.07752v1 fatcat:3no5j65puzb5nmx2rmj6t3ye7u


Claudio Comis Da Ronco, Ernesto Benini
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12  
The key issue for an efficient and reliable multi-objective evolutionary algorithm is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering  ...  In order to judge the performance of the GeDEA-II, a comparison with other different state-of-the-art multi-objective EAs was performed.  ...  Categories and Subject Descriptors General Terms Algorithms Keywords Evolutionary algorithms, Simplex Crossover, Multi Objective Optimization, Empirical -Comparison.  ... 
doi:10.1145/2330784.2330888 dblp:conf/gecco/RoncoB12 fatcat:qs2o6rmixbhgfa3lzcq7nag6sy

Comparison of Multi-objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis [article]

Sergio Garcia, Cong T Trinh
2019 bioRxiv   pre-print
In this study, we evaluated a library of the state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem.  ...  The modular cell design problem was mathematically formulated using a multi-objective optimization framework.  ...  Multi-objective evolutionary algorithms (MOEAs) are widely used techniques due to their flexibility and computational scalability.  ... 
doi:10.1101/616078 fatcat:o5fqobzw7bdwbbyfigobxx62ai

Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

Sergio Garcia, Cong T. Trinh
2019 Processes  
In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem.  ...  We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives.  ...  Two approaches can be used to solve MOPs, including multi-objective evolutionary algorithms (MOEAs) and mixed integer linear programming (MILP) algorithms.  ... 
doi:10.3390/pr7060361 fatcat:g2rjqkmz6vau5lfcjghy7nc5pa

Multi-Objective Evolutionary Design of Composite Data-Driven Models [article]

Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin, Pavel Vychuzhanin, Anna V. Kalyuzhnaya
2021 arXiv   pre-print
In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed.  ...  The experimental results confirm that a multi-objective approach to the model design allows achieving better diversity and quality of obtained models.  ...  . #1 -Comparison of the single-obj and multi-obj approaches Exp. #2 -comparison of the multi-obj selections types Exp. #3 -Comparison of multi-obj algorithms Comparasion with state-of-art and baseline  ... 
arXiv:2103.01301v2 fatcat:za4l4uxcyja3xi2o4enj4dgysi

Scaling up indicator-based MOEAs by approximating the least hypervolume contributor

Thomas Voß, Tobias Friedrich, Karl Bringmann, Christian Igel
2010 Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO '10  
Acknowledgments CI acknowledges support from the German Federal Ministry of Education and Research within the National Network Computational Neuroscience under grant number 01GQ0951.  ...  For this reason, they serve as second-level sorting criterion in many recent multi-objective evolutionary algorithms (MOEAs).  ...  INTRODUCTION It is known that the performance of multi-objective evolutionary algorithms (MOEAs) in general deteriorates with increasing number of objectives.  ... 
doi:10.1145/1830761.1830838 dblp:conf/gecco/VossFBI10 fatcat:3jtryl4dznfehavx7iqymxzapy

Empirical evaluation of pareto efficient multi-objective regression test case prioritisation

Michael G. Epitropakis, Shin Yoo, Mark Harman, Edmund K. Burke
2015 Proceedings of the 2015 International Symposium on Software Testing and Analysis - ISSTA 2015  
This paper presents an extensive empirical study of the effectiveness of multi objective test case prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger  ...  Unlike test suite minimisation, multi objective test case prioritisation has not been thoroughly evaluated.  ...  The technical contributions of this paper are as follows: • An empirical study of two multi objective evolutionary algorithms, as well as three state-of-the-art costcognisant additional greedy algorithms  ... 
doi:10.1145/2771783.2771788 dblp:conf/issta/EpitropakisYHB15 fatcat:dhvynkgs6bfdbd7glgbn37lmvu
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