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automatic Component-Wise Design of Multiobjective Evolutionary algorithms

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2016年第3期20卷 403-417页

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Libre Bruxelles CoDE IRIDIA Lab B-1050 Brussels Belgium

Multiobjective 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 these evolutionary algorithms 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 an MOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters.

关键词： automatic algorithm configuration evolutionary algorithms multiobjective optimization permutation flow shop problem (PFSP)

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Stochastic local search and parameters recommendation: a case study on flowshop problems

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第2期30卷 774-799页

作者： Pavelski, Lucas M. Delgado, Myriam Kessaci, Marie-Eleonore Freitas, Alex A. Univ Tecnol Fed Parana CPGEI Av Sete Setembro Curitiba Parana Brazil Univ Lille Cent Lille CNRS UMR 9189CRIStAL F-59000 Lille France Univ Kent Sch Comp Canterbury Kent England

The algorithm Selection Problem (ASP) considers the use of previous knowledge regarding problem features and algorithm performance to recommend the best strategy to solve a previously unseen problem. In the application context, the usual ASP for optimization considers recommending the best heuristics, whenever it faces a new similar problem instance, also known as the Per-Instance ASP. Although ASP for heuristic recommendation is not new, selecting heuristics and also their parameters, or the Per-instance algorithm configuration Problem, is still considered a challenging task. This paper investigates the use of meta-learning to recommend six different stochastic local searches and their parameters to solve several instances of permutation flowshop problems. The proposed approach uses several problem features, including fitness landscape metrics, builds the performance database using irace, and trains different multi-label recommendation models on a data set with more than 6000 flowshop problem instances. Experiments show that decision tree-based machine learning models achieve good performance, and the quality of the recommendations is capable of outperforming the state-of-the-art algorithm with tuned configuration.

关键词： algorithm selection problem automatic algorithm configuration flowshop stochastic local search

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Computational results for an automatically tuned CMA-ES with increasing population size on the CEC'05 benchmark set

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SOFT COMPUTING 2013年第6期17卷 1031-1046页

作者： Liao, Tianjun de Oca, Marco A. Montes Stutzle, Thomas Univ Libre Bruxelles CoDE IRIDIA Brussels Belgium Univ Delaware Dept Math Sci Newark DE 19716 USA

In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC'05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC'05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search.

关键词： automatic algorithm configuration CMA-ES Continuous optimization

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SATenstein: automatically building local search SAT solvers from components

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ARTIFICIAL INTELLIGENCE 2016年 232卷 20-42页

作者： KhudaBukhsh, Ashiqur R. Xu, Lin Hoos, Holger H. Leyton-Brown, Kevin Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Univ British Columbia Dept Comp Sci Vancouver BC V5Z 1M9 Canada

Designing high-performance solvers for computationally hard problems is a difficult and often time-consuming task. Although such design problems are traditionally solved by the application of human expertise, we argue instead for the use of automatic methods. In this work, we consider the design of stochastic local search (SLS) solvers for the propositional satisfiability problem (SAT). We first introduce a generalized, highly parameterized solver framework, dubbed SATenstein, that includes components drawn from or inspired by existing high-performance SLS algorithms for SAT. The parameters of SATenstein determine which components are selected and how these components behave;they allow SATenstein to instantiate many high-performance solvers previously proposed in the literature, along with trillions of novel solver strategies. We used an automated algorithm configuration procedure to find instantiations of SATenstein that perform well on several well-known, challenging distributions of SAT instances. Our experiments show that SATenstein solvers achieved dramatic performance improvements as compared to the previous state of the art in SLS algorithms;for many benchmark distributions, our new solvers also significantly outperformed all automatically tuned variants of previous state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： SAT Stochastic local search automatic algorithm configuration

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Large neighborhood search for multi-trip vehicle routing

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期255卷 422-441页

作者： Francois, Veronique Arda, Yasemin Crama, Yves Laporte, Gilbert Univ Liege HEC Management Sch QuantOM B-4000 Liege Belgium HEC Montreal Canada Res Chair Distribut Management Montreal PQ Canada

We consider the multi-trip vehicle routing problem, in which each vehicle can perform several routes during the same working shift to serve a set of customers. The problem arises when customers are close to each other or when their demands are large. A common approach consists of solving this problem by combining vehicle routing heuristics with bin packing routines in order to assign routes to vehicles. We compare this approach with a heuristic that makes use of specific operators designed to tackle the routing and the assignment aspects of the problem simultaneously. Two large neighborhood search heuristics are proposed to perform the comparison. We provide insights into the configuration of the proposed algorithms by analyzing the behavior of several of their components. In particular, we question the impact of the roulette wheel mechanism. We also observe that guiding the search with an objective function designed for the multi-trip case is crucial even when exploring the solution space of the vehicle routing problem. We provide several best known solutions for benchmark instances. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Transportation Vehicle routing Multi-trip Large neighborhood search automatic algorithm configuration

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A landscape-based analysis of fixed temperature and simulated annealing

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期304卷 395-410页

作者： Franzin, Alberto Stutzle, Thomas Univ Libre Bruxelles ULB LISA Brussels Belgium Univ Libre Bruxelles ULB IRIDIA Brussels Belgium

Since the introduction of Simulated Annealing (SA), researchers have considered variants that keep the same temperature value throughout the whole search and tried to determine whether this strategy can be more effective than the original cooling scheme. Several studied have tried to answer this question without a conclusive answer and without providing indications that could be useful for a practical imple-mentation. In this work, we address this question following an experimental approach, relating the char-acteristics of the algorithms with the characteristics of the landscapes they encounter. We use problem -independent landscape features to study the algorithmic behaviour across different problems. We con-sider three different objective functions and various instance classes and determine the conditions under which the fixed-temperature variant of SA can outperform its original counterpart and when SA is instead a better choice.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Metaheuristics Simulated annealing Stochastic local search automatic algorithm configuration Exploratory landscape analysis

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ABC-X: a generalized, automatically configurable artificial bee colony framework

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SWARM INTELLIGENCE 2017年第1期11卷 1-38页

作者： Aydm, Dogan Yavuz, Guecan Stuetzle, Thomas Dumlupinar Univ Dept Comp Engn TR-43000 Kutahya Turkey ULB CoDE IRIDIA Brussels Belgium

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.

关键词： Artificial bee colony automatic algorithm configuration algorithm framework Continuous optimization

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A unified ant colony optimization algorithm for continuous optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期234卷 597-609页

作者： Liao, Tianjun Stuetzle, Thomas de Oca, Marco A. Montes Dorigo, Marco Beijing Inst Syst Engn State Key Lab Complex Syst Simulat Beijing Peoples R China Univ Libre Bruxelles IRIDIA B-81050 Brussels Belgium Univ Delaware Dept Math Sci Newark DE 19716 USA

In this article, we propose UACOR, a unified ant colony optimization (ACO) algorithm for continuous optimization. UACOR includes algorithmic components from ACO(R), DACO(R) and IACO(R)-LS, three ACO algorithms for continuous optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms;in addition, from UACOR we can also generate new continuous ACO algorithms that have not been considered before in the literature. In fact, UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new ACO algorithms. To show the benefits of UACOR's flexibility, we automatically configure two new ACO algorithms, UACOR-s and UACOR-c, and evaluate them on two sets of benchmark functions from a recent special issue of the Soft Computing (SOCO) journal and the IEEE 2005 Congress on Evolutionary Computation (CEC'05), respectively. We show that UACOR-s is competitive with the best of the 19 algorithms benchmarked on the SOCO benchmark set and that UACOR-c performs superior to IPOP-CMA-ES and statistically significantly better than five other algorithms benchmarked on the CEC'05 set. These results show the high potential ACO algorithms have for continuous optimization and suggest that automatic algorithm configuration is a viable approach for designing state-of-the-art continuous optimizers. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Ant colony optimization Continuous optimization automatic algorithm configuration

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A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary algorithms

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EVOLUTIONARY COMPUTATION 2018年第4期26卷 621-656页

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stuetzle, Thomas Univ Fed Rio Grande do Norte IMD Natal RN Brazil Univ Fed Paraiba DCC CI Joao Pessoa PB Brazil Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium

Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a large number of algorithms and a rich literature on performance assessment tools to evaluate and compare them. Yet, newly proposed MOEAs are typically compared against very few, often a decade older MOEAs. One reason for this apparent contradiction is the lack of a common baseline for comparison, with each subsequent study often devising its own experimental scenario, slightly different from other studies. As a result, the state of the art in MOEAs is a disputed topic. This article reports a systematic, comprehensive evaluation of a large number of MOEAs that covers a wide range of experimental scenarios. A novelty of this study is the separation between the higher-level algorithmic components related to multi-objective optimization (MO), which characterize each particular MOEA, and the underlying parameters-such as evolutionary operators, population size, etc.-whose configuration may be tuned for each scenario. Instead of relying on a common or default parameter configuration that may be low-performing for particular MOEAs or scenarios and unintentionally biased, we tune the parameters of each MOEA for each scenario using automatic algorithm configuration methods. Our results confirm some of the assumed knowledge in the field, while at the same time they provide new insights on the relative performance of MOEAs for many-objective problems. For example, under certain conditions, indicator-based MOEAs are more competitive for such problems than previously assumed. We also analyze problem-specific features affecting performance, the agreement between performance metrics, and the improvement of tuned configurations over the default configurations used in the literature. Finally, the data produced is made publicly available to motivate further analysis and a baseline for future comparisons.

关键词： Multi-objective optimization evolutionary algorithms performance assessment automatic algorithm configuration

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Artificial bee colonies for continuous optimization: Experimental analysis and improvements

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SWARM INTELLIGENCE 2013年第4期7卷 327-356页

作者： Liao, Tianjun Aydin, Dogan Stutzle, Thomas Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium Dumlupinar Univ Dept Comp Engn TR-43000 Kutahya Turkey

The artificial bee colony (ABC) algorithm is a recent class of swarm intelligence algorithms that is loosely inspired by the foraging behavior of honeybee swarms. It was introduced in 2005 using continuous optimization problems as an example application. Similar to what has happened with other swarm intelligence techniques, after the initial proposal, several researchers have studied variants of the original algorithm. Unfortunately, often these variants have been tested under different experimental conditions and different fine-tuning efforts for the algorithm parameters. In this article, we review various variants of the original ABC algorithm and experimentally study nine ABC algorithms under two settings: either using the original parameter settings as proposed by the authors, or using an automatic algorithm configuration tool using a same tuning effort for each algorithm. We also study the effect of adding local search to the ABC algorithms. Our experimental results show that local search can improve considerably the performance of several ABC variants and that it reduces strongly the performance differences between the studied ABC variants. We also show that the best ABC variants are competitive with recent state-of-the-art algorithms on the benchmark set we used, which establishes ABC algorithms as serious competitors in continuous optimization.

关键词： Artificial bee colony Continuous optimization Experimental study automatic algorithm configuration

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