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The automatic Design of Multiobjective Ant Colony Optimization algorithms

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2012年第6期16卷 861-875页

作者： Lopez-Ibanez, Manuel Stuetzle, Thomas Univ Libre Bruxelles Inst Rech Interdisciplinaires & Dev Intelligence B-1050 Brussels Belgium

Multiobjective optimization problems are problems with several, typically conflicting, criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multiobjective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multiobjective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices, but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multiobjective algorithms.

关键词： Ant colony optimization (ACO) automatic algorithm configuration multiobjective optimization traveling salesman problem

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Capping methods for the automatic configuration of optimization algorithms

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COMPUTERS & OPERATIONS RESEARCH 2022年 139卷 1页

作者： de Souza, Marcelo Ritt, Marcus Lopez-Ibanez, Manuel Santa Catarina State Univ Dept Software Engn Florianopolis SC Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Malaga Sch Comp Sci Malaga Spain Univ Manchester Alliance Manchester Business Sch Manchester Lancs England

automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.

关键词： automatic algorithm configuration Capping methods Optimization algorithms Parameter tuning

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Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm

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APPLIED SOFT COMPUTING 2018年 73卷 328-343页

作者： Martins, Marcella S. R. Delgado, Myriam Luders, Ricardo Santana, Roberto Goncalves, Richard A. de Almeida, Carolina P. Univ Tecnol Fed Parana Curitiba Parana Brazil Univ Basque Country San Sebastian Spain State Univ Midwest Parana Guarapuava PR Brazil

The Hybrid Multi-objective Bayesian Estimation of Distribution algorithm (HMOBEDA) has shown to be very competitive for Many Objective Optimization Problems (MaOPs). The Probabilistic Graphic Model (PGM) of HMOBEDA expands the possibilities for exploration as it provides the joint probability of decision variables, objectives, and configuration parameters of an embedded local search. This work investigates different sampling mechanisms of HMOBEDA, applying the considered approaches to two different combinatorial MaOPs. Moreover, the paper provides a broad set of statistical analyses on its PGM model. These analyses have been carried out to evaluate how the interactions among variables, objectives and local search parameters are captured by the model and how information collected from different runs can be aggregated and explored in a Probabilistic Pareto Front. In experiments, two variants of HMOBEDA are compared with the original version, each one with a different set of evidences fixed during the sampling process. Results for instances of multi-objective knapsack problem with 2-5 and 8 objectives show that the best variant outperforms the original HMOBEDA in terms of convergence and diversity in the solution set. This best variant is then compared with five state-of-the-art evolutionary algorithms using the knapsack problem instances as well as a set of MNK-landscape instances with 2, 3, 5 and 8 objectives. HMOBEDA outperforms all of them.

关键词： Multi-objective optimization Estimation of distribution algorithms automatic algorithm configuration

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automatic Grammar-Based Design of Heuristic algorithms for Unconstrained Binary Quadratic Programming 1

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18th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： de Souza, Marcelo Ritt, Marcus Santa Catarina State Univ Ibirama SC Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

automatic methods have been applied to find good heuristic algorithms to combinatorial optimization problems. These methods aim at reducing human efforts in the trial-and-error search for promising heuristic strategies. We propose a grammar-based approach to the automatic design of heuristics and apply it to binary quadratic programming. The grammar represents the search space of algorithms and parameter values. A solution is represented as a sequence of categorical choices, which encode the decisions taken in the grammar to generate a complete algorithm. We use an iterated F-race to evolve solutions and tune parameter values. Experiments show that our approach can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

关键词： automatic algorithm configuration Grammatical evolution Metaheuristics

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Probabilistic Analysis of Pareto Front Approximation for a Hybrid Multi-objective Bayesian Estimation of Distribution algorithm 6

Probabilistic Analysis of Pareto Front Approximation for a H...

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6th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Martins, Marcella S. R. Delgado, Myriam Luders, Ricardo Santana, Roberto Goncalves, Richard A. de Almeida, Carolina P. Univ Tecnol Fed Parana Curitiba Parana Brazil Univ Basque Country Leioa Spain Midwest State Univ Parana Curitiba Parana Brazil

ISBN: (纸本)9781538624074

Metaheuristics that explore the decision variables space to construct probabilistic modeling from promising solutions, like estimation of distribution algorithms (EDAs), are becoming very popular in the context of Multi-objective Evolutionary algorithms (MOEAs). The probabilistic model used in EDAs captures certain statistics of problem variables and their interdependencies. Moreover, the incorporation of local search methods tends to achieve synergy of MOEAs' operators and local heuristics aiming to improve the performance. In this work, we aim to scrutinize the probabilistic graphic model (PGM) presented in Hybrid Multi-objective Bayesian Estimation of Distribution algorithm (HMOBEDA), which is based on a Bayesian network. Different from traditional EDA-based approaches, the PGM of HMOBEDA provides the joint probability of decision variables, objectives, and configuration parameters of an embedded local search. HMOBEDA has shown to be very competitive on instances of Multi-Objective Knapsack Problem (MOKP), outperforming state-of-the-art approaches. Two variants of HMOBEDA are proposed in this paper using different sample methods. We aim to compare the learnt structure in terms of the probabilistic Pareto Front approximation produced at the end of evolution. Results on instances of MOKP with 2 to 8 objectives show that both proposed variants outperform the original approach, providing not only the best values for hypervolume and inverted generational distance indicators, but also a higher diversity in the solution set.

关键词： Multi-objective optimization estimation of distribution algorithms automatic algorithm configuration

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Evaluating random forest models for irace 17

Evaluating random forest models for irace

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Caceres, Leslie Perez Bischl, Bernd Stuetzle, Thomas Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium Ludwig Maximilians Univ Munchen Dept Stat Munich Germany

ISBN: (纸本)9781450349390

automatic algorithm configurators can greatly improve the performance of algorithms by effectively searching the parameter space. As algorithm configuration tasks can have large parameter spaces and the execution of candidate algorithm configurations is often very costly in terms of computation time, further improvements in the search techniques used by automatic configurators are important and increase the applicability of available configuration methods. One common technique to improve the behavior of search methods when evaluations are computationally expensive are surrogate model techniques. These models are able to exploit the scarce available data and help to direct the search towards evaluating the most promising candidate configurations. In this paper, we study the use of random forests models as surrogate models in irace, a flexible automatic configuration tool based on iterated racing that has been successfully applied in the literature. We evaluate the performance of the random forest model using different settings when trained with data obtained from the irace configuration process and we evaluate their performance under similar conditions as in the configuration process. This preliminary work aims at providing guidelines for the incorporation of random forest to the configuration process of irace.

关键词： automatic algorithm configuration parameter tuning irace random forests

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An automatically Designed Recombination Heuristic for the Test-Assignment Problem

An Automatically Designed Recombination Heuristic for the Te...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： de Souza, Marcelo Ritt, Marcus Santa Catarina State Univ UDESC Dept Software Engn Ibirama Brazil Fed Univ Rio Grande do Sul UFRGS Inst Informat INF Porto Alegre RS Brazil

ISBN: (纸本)9781509060177

A way of minimizing the opportunity of cheating in exams is to assign different tests to students. The likelihood of cheating then depends on the proximity of the students' desks, and the similarity of the tests. The test-assignment problem is to find an assignment of tests to desks that minimizes that total likelihood of cheating. The problem is a variant of a graph coloring problem and is NP-hard. We propose a new heuristic solution for this problem. Our approach differs from the usual way of designing heuristics in two ways. First, we reduce test-assignment to the more general unconstrained binary quadratic programming. Second, we search for a good heuristic using an automatic algorithm configuration tool that evolves heuristics in a space of algorithms built from known components for binary quadratic programming. The best hybrid heuristics found repeatedly recombine elements of a population of elite solutions and improve them by a tabu search. Computational tests suggest that the resulting algorithms are competitive with existing heuristics that have been designed manually.

关键词： test-assignment binary quadratic programming automatic algorithm configuration metaheuristics

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automatic Design of Heuristics for Minimizing the Makespan in Permutation Flow Shops

Automatic Design of Heuristics for Minimizing the Makespan i...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Brum, Artur Ritt, Marcus Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9781509060177

The automatic design of heuristic search methods has been applied successfully to many optimization problems. In this paper we study the application of automatic algorithm configuration to the permutation flow shop scheduling problem with makespan minimization. Our approach consists in using a grammar to determine how to combine individual algorithmic components into an iterated local search, coupled with a parametric representation for the instantiations of such a grammar. To explore the algorithmic search space we employ a procedure based on racing. The obtained algorithms are evaluated on two well-known benchmarks and compared to state-of-the-art heuristics.

关键词： automatic algorithm configuration iterated local search permutation flow shop scheduling problem makespan

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Flowshop NEH-Based Heuristic Recommendation 1

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21st European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Pavelski, Lucas Marcondes Kessaci, Marie-Eleonore Delgado, Myriam Univ Tecnol Fed Parana Av Sete Setembro 3165 BR-80230901 Curitiba Brazil Univ Lille Cent Lille CNRS UMR CRIStAL 9189 F-59000 Lille France

ISBN: (数字)9783030729042

ISBN: (纸本)9783030729035;9783030729042

Flowshop problems (FSPs) have many variants and a broad set of heuristics proposed to solve them. Choosing the best heuristic and its parameters for a given FSP instance can be very challenging for practitioners. Per-instance algorithm configuration (PIAC) approaches aim at recommending the best algorithm configuration for a particular instance problem. This paper presents a PIAC methodology for building models to automatically configure the Nawaz, Encore, and Ham (NEH) algorithm which proved to be a good choice in most FSP variants (especially when they are used to provide initial solutions). We use irace to build the performance dataset (problem features. algorithm configuration), while training Decision Tree and Random Forest models to recommend NEH configurations on unseen problems of the test set. Results show that the recommended heuristics have good performance, especially those by random forest models considering parameter dependencies.

关键词： Flowshop Heuristics automatic algorithm configuration Parameter dependencies Machine learning

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A Bilevel Optimization Approach to Automated Parameter Tuning 14

A Bilevel Optimization Approach to Automated Parameter Tunin...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Sinha, Ankur Malo, Pekka Xu, Peng Deb, Kalyanmoy Aalto Univ Sch Business Dept Informat & Serv Econ Helsinki 00076 Finland

ISBN: (纸本)9781450326629

Many of the modern optimization algorithms contain a number of parameters that require tuning before the algorithm can be applied to a particular class of optimization problems. A proper choice of parameters may have a substantial effect on the accuracy and efficiency of the algorithm. Until recently, parameter tuning has mostly been performed using brute force strategies, such as grid search and random search. Guesses and insights about the algorithm are also used to find suitable parameters or suggest strategies to adjust them. More recent trends include the use of meta-optimization techniques. Most of these approaches are computationally expensive and do not scale when the number of parameters increases. In this paper, we propose that the parameter tuning problem is inherently a bilevel programming problem. Based on this insight, we introduce an evolutionary bilevel algorithm for parameter tuning. A few commonly used optimization algorithms (Differential Evolution and Nelder-Mead) have been chosen as test cases, whose parameters are tuned on a number of standard test problems. The bilevel approach is found to quickly converge towards the region of efficient parameters. The code for the proposed algorithm can be accessed from the website http://***.

关键词： Bilevel optimization evolutionary algorithms parameter tuning automatic algorithm configuration

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