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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Minisci, Edmondo Vasile, Massimiliano Univ Glasgow Sch Engn James Watt South Bldg Glasgow G12 8QQ Lanark Scotland Univ Strathclyde Dept Mech Engn Glasgow G1 1XJ Lanark Scotland

ISBN: (纸本)9781450305570

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimisation technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that minimise the mean value of the maximum heat flux, the mean value of the maximum achievable distance, and the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimisation process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach.

关键词： multi-objective evolutionary algorithms Robust multi Disciplinary Design Meta-modelling Optimal Control Unmanned Space Vehicle

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Extracting Variability-Safe Feature Models from Source Code Dependencies in System Variants 15

Extracting Variability-Safe Feature Models from Source Code ...

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17th Genetic and evolutionary Computation Conference (GECCO)

作者： Assuncao, Wesley K. G. Lopez-Herrejon, Roberto E. Linsbauer, Lukas Vergilio, Silvia R. Egyed, Alexander Univ Fed Parana DINF BR-81531980 Curitiba Parana Brazil Technol Fed Univ Parana COINF BR-85902490 Toledo Brazil Johannes Kepler Univ Linz ISSE A-4040 Linz Austria

ISBN: (纸本)9781450334723

To effectively cope with increasing customization demands, companies that have developed variants of software systems are faced with the challenge of consolidating all the variants into a Software Product Line, a proven development paradigm capable of handling such demands. A crucial step in this challenge is to reverse engineer feature models that capture all the required feature combinations of each system variant. Current research has explored this task using propositional logic, natural language, and search-based techniques. However, using knowledge from the implementation artifacts for the reverse engineering task has not been studied. We propose a multi-objective approach that not only uses standard precision and recall metrics for the combinations of features but that also considers variability-safety, i.e. the property that, based on structural dependencies among elements of implementation artifacts, asserts whether all feature combinations of a feature model are in fact well-formed software systems. We evaluate our approach with five case studies and highlight its bene fits for the software engineer.

关键词： Reverse Engineering Feature Models multi-objective evolutionary algorithms

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Solving the aircraft engine maintenance scheduling problem using a multi-objective evolutionary algorithm 05

Solving the aircraft engine maintenance scheduling problem u...

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Proceedings of the 7th annual workshop on Genetic and evolutionary computation

作者： Mark P. Kleeman Gary B. Lamont Air Force Institute of Technology Wright-Patterson Air Force Base Dayton OH

ISBN: (纸本)9781450378000

Scheduling problems are a very common research topic. This is because, for efficiency reasons, our world relies heavily on schedules and deadlines. Aircraft engine maintenance is no exception. The United States Air Force has many planes that it must keep up and running. But with the downsizing that has occurred in recent years, the number of planes that are operational has become more critical. This means that every effort needs to be made to ensure that not only are the engines repaired in an efficient manner, but that their component's scheduled maintenance cycles are in sync so that the engine has fewer trips to the logistics maintenance center.

关键词： aircraft engine scheduling job shop scheduling multi-objective evolutionary algorithms flow shop military application

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Exploring multi-objective Cellular Genetic algorithms in Community Detection Problems 21st

Exploring Multi-objective Cellular Genetic Algorithms in Com...

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21st International Conference on Intelligent Data Engineering and Automated Learning

作者： Pedemonte, Martin Panizo-LLedot, Angel Bello-Orgaz, Gema Camacho, David Univ Republica InCo Montevideo Uruguay Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

ISBN: (纸本)9783030623647;9783030623654

Interest in network analysis has not stopped increasing over the last decade. The Community Detection Problem (CDP) has been a hot topic in network analysis, so many different approaches have been proposed. Among them, optimization methods have proven to be highly effective for this task. Traditionally, the CDP has been tackled as a single-objective optimization problem. Nevertheless, this trend has started to change, and new methods have appeared following multi-objective approaches. Genetic algorithms have been applied to the CDP with relative success, especially NSGA-II. However, cellular Genetic algorithms (cGAs) have yet received little attention. In cGAs, the population is structured in small overlapping neighborhoods producing a slow spread of high-quality solutions. The main contribution of this paper is understanding if the smooth diffusion scheme of MoCell (a multi-objective cGA) can provide any benefit over current multi-objective GAs for the CDP. To verify the effectiveness of MoCell, an evaluation was conducted on 21 synthetically generated networks and two real-world ones. The experiments show that MoCell is able to outperform NSGA-II, especially in large networks scenarios.

关键词： Static community detection problem Network analysis multi-objective evolutionary algorithms Cellular genetic algorithms

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Rank-Based Nondomination Set Identification with Preprocessing

Rank-Based Nondomination Set Identification with Preprocessi...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Palakonda, Vikas Mallipeddi, Rammohan Kyungpook Natl Univ Sch Elect Engn Daegu 702701 South Korea

ISBN: (纸本)9783319410098;9783319410081

In multi-objective optimization, finding a nondomination set is a computationally expensive process and the complexity grows with the population size. In this paper, we propose a nondomination set identification method with (a) rank-based preprocessing step where the obvious dominant solutions are eliminated and (b) better order of comparison based on the average rank so that the number of comparisons can be significantly reduced. In preprocessing, the maximum rank information of the solutions that are best in each individual objectives is used. In addition, during nondomination set identification process to check if a solution is nondominant the solution is compared only with solutions that are better in terms of average rank. The experiment results demonstrate the effectiveness of the proposed method in identifying the nondominant set in less number of comparisons.

关键词： multi-objective evolutionary algorithms Nondomination set identification Preprocessing step Rank-based sorting

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On the adaptation of reference sets using niching and pair-potential energy functions for multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2023年 83卷

作者： Marquez-Vega, Luis A. Falcon-Cardona, Jesus Guillermo Osuna, Edgar Covantes Tecnol Monterrey Sch Engn & Sci Ave Eugenio Garza Sada 2501 Monterrey 64849 Nuevo Leon Mexico

multi-objective evolutionary algorithms (MOEAs) have shown a good capability to approximate the Pareto front (PF) of complex multi-objective optimization problems (MOPs). Recently, several decomposition-based, indicator-based, and reference set-based MOEAs have been proposed to tackle MOPs with four or more objective functions, known as many-objective optimization problems (MaOPs). Some well-known MOEAs from these design strategies use a predefined reference set generated with uniformly distributed weight vectors on a unit simplex. These MOEAs have demonstrated good convergence, coverage, and uniformity of solutions on MOPs with regular PF shapes, i.e., simplex-like shapes. However, it has been shown that their performance degrades on MOPs with irregular PF shapes. In this paper, we proposed a pluggable reference set adaptation method based on niching and pair-potential energy functions (PPFs), called Adak, to enhance the performance of these MOEAs on MOPs with irregular PF shapes while maintaining their good behavior on MOPs with regular PF shapes. Our adaptation method was validated by plugging it into three well-known MOEAs that use a predefined weight vector-based reference set. We perform an empirical study using different PPFs for our adaptation method on the DTLZ, WFG, Minus-DTLZ, Minus-WFG, IMOP, and VNT test suites. Our experimental results show the capability of our adaptation method to promote an invariant performance regardless of the PF shape. Additionally, we show that MOEAs with our adaptation method have a competitive performance against state-of-the-art MOEAs.

关键词： multi-objective evolutionary algorithms Reference sets Weight vectors Adaptation methods Pair-potential energy functions

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Enhancing soft computing techniques to actively address imbalanced regression problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期234卷

作者： Arteaga, Maria Gacto, Maria Jose Galende, Marta Alcala-Fdez, Jesus Alcala, Rafael Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Granada Dept Software Engn E-18071 Granada Spain CARTIF Ctr Tecnol Parque Tecnol Boecillo Valladolid 47151 Spain Univ Valladolid Sch Ind Engn Dept Syst Engn & Control Valladolid 47011 Spain

While research in the area of imbalance, which is understood as classes that are not equally represented, is mainly addressed in classification, it has hardly been studied in regression, where data maldistribution, or imbalance, can be defined as the existence of some specific subdomains of the output variable misrepresented in the training data set, resulting in low accuracy for new instances within these subdomains. The small amount of state-of-the-art techniques are "passive", meaning they are only applied in preprocessing. In this contribution, we propose two new specific evolutionary algorithms based on fuzzy rules to "actively"address imbalanced regression problems and improve the overall performance of the algorithms instead of just addressing the imbalance problem. The results obtained after applying statistical tests to 32 regression datasets that handle more than 3000 partitions show the effectiveness of the proposed methods when compared to the best previous proposal, a passive method called SMOGN. We can conclude: (1) we cannot affirm, since the equality hypotheses have not been rejected, that there are significant performance differences between using stratified and non-stratified data, thus we will use stratification to preserve a minimum representation of the minority set, (2) both fuzzy rule-based methods obtain better results in terms of the imbalance metric when using SMOGN, but in both methods this incurs a cost in accuracy (with confidence scores of over 99.0%), and (3) the proposed methods outperform those using SMOGN as they get slightly better results in the imbalance metric, with better average ranks in both proposals, and obtain significantly better results in global accuracy, that is, all the performance metrics studied improve statistically with a confidence score of over 99.0%, with the exception of one metric, which scores above 90.0%.

关键词： Imbalanced regression multi-objective evolutionary algorithms Fuzzy rule-based systems

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Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

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APPLIED SOFT COMPUTING 2020年第PartA期97卷 106760-106760页

作者： de Santiago Junior, Valdivino Alexandre Ozcan, Ender de Carvalho, Vinicius Renan Inst Nacl Pesquisas Espaciais INPE Coordenacao Pesquisa Aplicada & Desenvolvimento T Av Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil Univ Nottingham Sch Comp Sci Computat Optimisat & Learning COL Lab Wollaton Rd Nottingham NG8 1BB England Univ Sao Paulo Escola Politecn Poli Av Prof Luciano Gualberto 380 BR-380 Sao Paulo SP Brazil

In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement LearnIng, Balanced Heuristic Selection and Group Decision AccEptance (HRISE), controlling a set of multi-objective evolutionary algorithms (MOEAs) as Low-Level (meta)Heuristics (LLHs). Along with the use of multiple MOEAs, we believe that having a robust LLH selection method as well as several move acceptance methods at our disposal would lead to an improved general-purpose method producing most adequate solutions to the problem instances across multiple domains. We present two learning hyper-heuristics based on the HRISE framework for multi-objective optimisation, each embedding a group decision-making acceptance method under a different rule: majority rule (HRISE_M) and responsibility rule (HRISE_R). A third hyper-heuristic is also defined where both a random LLH selection and a random move acceptance strategy are used. We also propose two variants of the late acceptance method and a new quality indicator supporting the initialisation of selection hyper-heuristics using low computational budget. An extensive set of experiments were performed using 39 multi-objective problem instances from various domains where 24 are from four different benchmark function classes, and the remaining 15 instances are from four different real-world problems. The cross-domain search performance of the proposed learning hyperheuristics indeed turned out to be the best, particularly HRISE_R, when compared to three other selection hyper-heuristics, including a recently proposed one, and all low-level MOEAs each run in isolation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Hyper-heuristic Reinforcement learning Balanced heuristic selection Group decision-making multi-objective evolutionary algorithms multi-objective optimisation

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A modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2024年 86卷

作者： Gao, Xiaoxin He, Fazhi Wang, Feng Wang, Xiaoting Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China JD Com Inc JD Technol Beijing Peoples R China

multi-objective evolutionary algorithms have demonstrated promising performance in solving multi/manyobjective problems. However, their performance decreases sharply when dealing with multi-objective optimization problems with hundreds or thousands of decision variables, which prevent them from quickly converging to the Pareto front. To this end, this article proposes a modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization (LSMCSO-SS). In the initialization phase of the algorithm, we propose a space sampling method, which samples a set of individuals according to the upper and lower bounds of the decision space. Then, they are added to the initial population to guide the evolution of the algorithm. In the iteration process of the algorithm, we propose a modified competitive swarm optimizer (CSO). Different from the original CSO algorithm, we add a new velocity component to the losers to further improve the convergence speed. In the experiments, we compare our algorithm with seven state -of -the -art large-scale multi-objective evolutionary algorithms based on the inverted generational distance plus (IGD+) indicator upon nine large-scale multi-objective optimization benchmark problems with up to 5000 decision variables, and the numerous experimental results manifest that the proposed method performs the best on most test instances, which further demonstrates that it outperforms all the seven comparison algorithms.

关键词： multi-objective evolutionary algorithms Large-scale optimization Competitive swarm optimizer (CSO)

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Cooperative coevolutionary multi-guide particle swarm optimization algorithm for large-scale multi-objective optimization problems

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SWARM AND evolutionary COMPUTATION 2023年 78卷

作者： Madani, Amirali Engelbrecht, Andries Ombuki-Berman, Beatrice Brock Univ Dept Comp Sci St Catharines ON Canada Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Stellenbosch Univ Comp Sci Div Stellenbosch South Africa

Many problems that are encountered in real-life applications consist of two or three conflicting objectives and many decision variables. multi-guide particle swarm optimization (MGPSO) is a novel meta-heuristic for multi -objective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other state-of-the-art multi-objective optimization algorithms for low-dimensional (and even many-objective) problems. However, a recent study has shown that MGPSO does not scale well when the number of decision variables is increased. This paper proposes a new scalable MGPSO-based algorithm, termed cooperative coevolutionary multi-guide particle swarm optimization (CCMGPSO), that incorporates ideas from cooperative coevolution (CC). CCMGPSO uses new techniques to spend less computational budget by periodically assigning only one CC-based subswarm to each objective (as opposed to using numerous CC -based subswarms). A detailed empirical study on well-known benchmark problems comparing the CCMGPSO with various state-of-the-art large-scale multi-objective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems with reference to the inverted generational distance (IGD) metric.

关键词： Particle swarm optimization multi-objective optimization multi-objective evolutionary algorithms Large-scale multi-objective optimization Cooperative coevolution multi-guide particle swarm optimization

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