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PSO-X: A Component-Based Framework for the automatic design of Particle Swarm Optimization algorithms

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第3期26卷 402-416页

作者： Camacho-Villalon, Christian L. Dorigo, Marco Stutzle, Thomas Univ Libre Bruxelles Inst Rech Interdisciplinaires & Dev Intelligence B-1050 Brussels Belgium

The particle swarm optimization (PSO) algorithm has been the object of many studies and modifications for more than 25 years. Ranging from small refinements to the incorporation of sophisticated novel ideas, the majority of modifications proposed to this algorithm have been the result of a manual process in which developers try new designs based on their own knowledge and expertise. However, manually introducing changes is very time consuming and makes the systematic exploration of all the possible algorithm configurations a difficult process. In this article, we propose to use automatic design to overcome the limitations of having to manually find performing PSO algorithms. We develop a flexible software framework for PSO, called PSO-X, which is specifically designed to integrate the use of automatic configuration tools into the process of generating PSO algorithms. Our framework embodies a large number of algorithm components developed over more than 25 years of research that have allowed PSO to deal with a large variety of problems, and uses irace, a state-of-the-art configuration tool, to automatize the task of selecting and configuring PSO algorithms starting from these components. We show that irace is capable of finding high-performing instances of PSO algorithms never proposed before.

关键词： Topology Tools Particle swarm optimization Software algorithms Optimization Manuals Software automatic algorithm design continuous optimization particle swarm optimization (PSO)

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automatic design of Evolutionary algorithms based on Entropy Triggers

Automatic Design of Evolutionary Algorithms based on Entropy...

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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： da Silva, Guilherme Ribeiro Basgalupp, Marcio P. Lorena, Ana Carolina Univ Fed Sao Paulo Inst Ciencia & Tecnol Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781509060177

The field of automatic algorithm design has received increasing attention in recent years. From a multitude of available algorithms, a researcher can effectively design a new one customized to his/her own problem. For this, hyper-heuristics techniques have proven to be useful. Their main objective is to search in the space of heuristics rather than in the problem solution space. The present paper proposes a hyper-heuristic for the automatic design of evolutionary algorithms supported by the use of an entropy metric. This metric is used as a trigger mechanism for switching between the algorithms components, aiding the formation of the new hybrid algorithm.

关键词： hyper-heuristics automatic algorithm design grammatical-evolution automatic algorithm selection

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automatically designing State-of-the-Art Multi- and Many-Objective Evolutionary algorithms

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EVOLUTIONARY COMPUTATION 2020年第2期28卷 195-226页

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Fed Rio Grande do Norte Inst Metropole Digital IMD Natal RN Brazil Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium

A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization. Our work is based on two main considerations. The first is that high-performing algorithms can be obtained from a configurable algorithmic framework in an automated way. The second is that multiple performance metrics may be required to guide this automatic design process. In the first part of this work, we extend our previously proposed algorithmic framework, increasing the number of MOEAs, underlying evolutionary algorithms, and search paradigms that it comprises. These components can be combined following a general MOEA template, and an automatic configuration method is used to instantiate high-performing MOEA designs that optimize a given performance metric and present state-of-the-art performance. In the second part, we propose a multiobjective formulation for the automatic MOEA design, which proves critical for the context of many-objective optimization due to the disagreement of established performance metrics. Our proposed formulation leads to an automatically designed MOEA that presents state-of-the-art performance according to a set of metrics, rather than a single one.

关键词： Multiobjective optimization evolutionary algorithms automatic algorithm design

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Revisiting simulated annealing: A component-based analysis

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COMPUTERS & OPERATIONS RESEARCH 2019年 104卷 191-206页

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

Simulated Annealing (SA) is one of the oldest metaheuristics and has been adapted to solve many combinatorial optimization problems. Over the years, many authors have proposed both general and problem-specific improvements and variants of SA. We propose to accumulate this knowledge into automatically configurable, algorithmic frameworks so that for new applications that wealth of alternative algorithmic components is directly available for the algorithm designer without further manual intervention. Here, we describe SA as an ensemble of algorithmic components, and describe SA variants from the literature within these components. We show the advantages of our proposal by (i) implementing existing algorithmic components of variants of SA, (ii) studying SA algorithms proposed in the literature, (iii) improving SA performance by automatically designing new state-of-the-art SA implementations and (iv) studying the role and impact of the algorithmic components based on experimental data. Our experiments consider three common combinatorial optimization problems, the quadratic assignment problem and two variants of the permutation flow shop problem. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Simulated annealing Metaheuristics Stochastic local search automatic algorithm design Experimental analysis

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Interactive evolutionary computation with minimum fitness evaluation requirement and offline algorithm design

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SPRINGERPLUS 2016年第1期5卷 192页

作者： Ishibuchi, Hisao Sudo, Takahiko Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

In interactive evolutionary computation (IEC), each solution is evaluated by a human user. Usually the total number of examined solutions is very small. In some applications such as hearing aid design and music composition, only a single solution can be evaluated at a time by a human user. Moreover, accurate and precise numerical evaluation is difficult. Based on these considerations, we formulated an IEC model with the minimum requirement for fitness evaluation ability of human users under the following assumptions: They can evaluate only a single solution at a time, they can memorize only a single previous solution they have just evaluated, their evaluation result on the current solution is whether it is better than the previous one or not, and the best solution among the evaluated ones should be identified after a pre-specified number of evaluations. In this paper, we first explain our IEC model in detail. Next we propose a (mu + 1) ES-style algorithm for our IEC model. Then we propose an offline meta-level approach to automated algorithm design for our IEC model. The main feature of our approach is the use of a different mechanism (e.g., mutation, crossover, random initialization) to generate each solution to be evaluated. Through computational experiments on test problems, our approach is compared with the (mu + 1) ES-style algorithm where a solution generation mechanism is pre-specified and fixed throughout the execution of the algorithm.

关键词： Interactive evolutionary computation Interactive algorithms automatic algorithm design Meta-level evolutionary algorithms

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automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints

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OPERATIONS RESEARCH PERSPECTIVES 2021年 8卷

作者： Pagnozzi, Federico Stutzle, Thomas Univ Libre Bruxelles ULB IRIDIA 50 Av F RooseveltCP 194-6 B-1050 Brussels Belgium

automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to combine algorithmic components following a set of rules defined as a context-free grammar. In this paper we use the same system to tackle two of the most studied additional constraints for these objectives: sequence dependent setup times and no-idle constraint. Additional components have been added to adapt the system to the new problems while keeping intact the grammar structure and the experimental setup. The experiments show that the generated algorithms outperform the state of the art in each case.

关键词： Combinatorial optimization Stochastic local search algorithms automatic algorithm design Permutation flowshop problem No-idle Sequence dependent setup times

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Offline automatic Parameter Tuning of MOEA/D Using Genetic algorithm

Offline Automatic Parameter Tuning of MOEA/D Using Genetic A...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Pang, Lie Meng Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781728124858

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is one of the favorite algorithms in the evolutionary computation community. In this paper, a genetic algorithm is used to automatically tune the parameters for MOEA/D in an offline manner. We consider a version of MOEA/D with a normalization mechanism and two neighborhood structures (for mating and replacement). Our experimental results show that the automatically obtained implementation of MOEA/D outperforms MOEA/D with the default settings in their applications to the DTLZ and WFG test suites. The obtained implementation for each test problem also allows us to discover some potentially good parameter values that can lead to the performance improvement of MOEA/D on certain test problems.

关键词： automatic algorithm design hyper-heuristics MOEA/D multiobjective optimization offline parameter tuning

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Reacting and Adapting to the Environment: designing Autonomous Methods for Multi-Objective Combinatorial Optimisation

Reacting and Adapting to the Environment: Designing Autonomo...

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作者： Aymeric Blot Universite de Lille

学位级别：博士

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself. While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms. We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.

关键词： automatic algorithm design multi-objective optimisation combinat-orial optimisation metaheuristics local search algorithms

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designing Heuristic-Based Tuners for PID Controllers in automatic Voltage Regulator Systems Using an Automated Hyper-Heuristic Approach

Designing Heuristic-Based Tuners for PID Controllers in Auto...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zambrano-Gutierrez, Daniel F. Molina-Porras, Alberto C. Avina-Cervantes, Juan G. Correa, Rodrigo Cruz-Duarte, Jorge M. Tecnologico de Monterrey Monterrey64849 Mexico University of Guanajuato Salamanca36885 Mexico Universidad Industrial de Santander Bucaramanga680002 Colombia

ISBN: (纸本)9781665430654

Engineering processes often require optimizing model variables for satisfactory solutions. Reliable approaches exist in literature but are application-dependent. In that sense, metaheuristics have been proven to deliver outstanding results while imposing a low computing burden. However, choosing the most suitable one from the many available can overwhelm even experts. This study implements a methodology that automatically tailors a problem-based metaheuristic through a hyper-heuristic approach. We select the tuning problem of a Proportional Integral Derivative controller as a case study for achieving the best stable features in an automatic Voltage Regulator system. The numerical results demonstrate the reliability and potential of the implemented methodology in solving control system tuning. Plus, we conduct an in-depth quantitative comparison with recent works in the literature that support those conclusions. © 2023 IEEE.

关键词： automatic algorithm design Complex Systems Control Engineering Metaheuristics Parameter Tuning

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automatic design of algorithms for the traveling salesman problem

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COGENT ENGINEERING 2016年第1期3卷 Article: 1255165页

作者： Loyola, Cristian Sepulveda, Mauricio Solar, Mauricio Lopez, Pierre Parada, Victor Univ Santiago Chile Dept Ingn Informat 3659 Ave Ecuador Santiago Chile Univ Tecn Federico Santa Maria Dept Informat Campus San JoaquinAve Vicuna Mackenna 3939 Santiago Chile Univ Toulouse CNRS LAAS Toulouse France

The automatic generation of procedures for combinatorial optimization problems is emerging as a new alternative to address the hardest problems of this class. One of these problems still offering great computational difficulty is the traveling salesman problem. Its simple presentation masks the great difficulty that exists when solving it numerically. The results obtained so far for this problem are based on the hybridization of known heuristics. However, there is still a need for an experimental breakthrough in the study of all of the possible combinations of heuristics, which represents a huge search space. In this paper, we explore this space using evolutionary computing to automatically design new algorithms for the problem. We carried out a computational experiment to produce the algorithms that not only are competitive with some of the existing heuristics but that also contain several novel structures that directly influence performance.

关键词： traveling salesman problem automatic algorithm design heuristics hyperheuristics genetic programming evolutionary computing

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