Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization

Bezerra, Leonardo C. T.; López-Ibáñez, Manuel; Stützle, Thomas

doi:10.1007/978-3-319-10762-2_50

Cited by 21 publications

(23 citation statements)

References 18 publications

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“…In a preliminary evaluation of our proposed framework [23], we applied the automatic MOEA design for tackling four multi-objective permutation flow shop problems (MO-PFSP), a well-known class of multi-objective combinatorial problems. Although many MOEAs have not been designed with combinatorial optimization problems in mind, many of the MOEAs we considered in Section III have been adapted to such problems using problem-specific variation operators [31].…”

Section: Automatically Designing Moeas For Combinatorial Optimizamentioning

confidence: 99%

“…In addition, the testing set considers instances with 5, 10, and 20 machines, while the tuning set uses only instances with 20 machines. For full details we refer to the original paper [23].…”

Section: A Experimental Setupmentioning

confidence: 99%

“…In a preliminary version of this work [23], we considered four multi-objective variants of the permutation flow shop problem (PFSP), varying the number and nature of the objectives. Since MOEAs from the literature were not originally devised for such problems, it is not surprising that we were able to generate AutoMOEAs that outperformed them.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms

Bezerra

López-Ibáñez

Stützle

2016

IEEE Trans. Evol. Computat.

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Abstract-Multi-objective evolutionary algorithms (MOEAs) are typically proposed, studied and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of MOEAs can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose a MOEA template and a new conceptual view of MOEA components that surpasses existing frameworks in both the number of MOEAs that can be instantiated from the template and the flexibility to produce novel MOEA designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for multi-objective continuous and combinatorial optimization problems. The automatically designed MOEAs are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters.

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Section: Automatically Designing Moeas For Combinatorial Optimizamentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms

Bezerra

López-Ibáñez

Stützle

2016

IEEE Trans. Evol. Computat.

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“…Given an application, designers can then tailor algorithms to their target application. To demonstrate the potential of the component-wise design, the authors used an automatic configuration tool to automatically design various AutoMOEAs for the most-used continuous benchmarks [5], as well as for several combinatorial problems [6]. In particular, the AutoMOEAs designed for five-objective problems presented outstanding performance, matching the best-performing algorithms for the WFG benchmark, and outperforming all of them for the DTLZ benchmark [5].…”

Section: Search Paradigms In Multi-objective Optimizationmentioning

confidence: 99%

Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms

Bezerra

López-Ibáñez

Stützle

2015

Lecture Notes in Computer Science

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Abstract.A main focus of current research on evolutionary multiobjective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to the development of more effective algorithms, decomposition and component-wise design have presented particularly good results. But how do they compare? In this work, we conduct a systematic analysis that compares algorithms produced using the MOEA/D decomposition-based framework and the AutoMOEA component-wise design framework. In particular, we identify a version of MOEA/D that outperforms the best known MOEA/D algorithm for several scenarios and confirms the effectiveness of decomposition on problems with three objectives. However, when we consider problems with five objectives, we show that MOEA/D is unable to outperform SMS-EMOA, being often outperformed by it. Conversely, automatically designed AutoMOEAs display competitive performance on three-objective problems, and the best and most robust performance among all algorithms considered for problems with five objectives.

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“…Multiple research works are published annually in this area, both in journals ([Kasperski & Zieliński(2015)]), and conferences ( [Bezerra et al(2014) Bezerra, López-Ibáñez & Stützle]), and also in books ([Levin(2015)]).…”

Section: Introductionmentioning

confidence: 99%

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

Osaba

Yang

Díaz

et al. 2016

Engineering Applications of Artificial Intelligence

289

115

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Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's ttest, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

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Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization

Cited by 21 publications

References 18 publications

Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms

Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms

Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

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