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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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Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

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ENGINEERING OPTIMIZATION 2014年第8期46卷 1032-1051页

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen Thailand

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.

关键词： comparative performance multi-objective evolutionary algorithms multi-objective truss optimization hybrid evolutionary algorithms population-based incremental learning

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Enhancing soft computing techniques to actively address imbalanced regression problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 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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Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 104卷 101818-101818页

作者： Jimenez, Fernando Palma, Jose Sanchez, Gracia Marin, David Francisco Palacios, M. D. Lucia Lopez, M. D. Univ Murcia Fac Comp Sci Artificial Intelligence & Knowledge Engn Grp Murcia Spain Univ Hosp Getafe Madrid Spain

Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision-making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making

关键词： Feature selection multi-objective evolutionary algorithms multivariate time series Antibiotic resistance forecasting multiple criteria decision making

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Subsonic Jet Noise Prediction in Near and Far Field with Optimized Wave-Packet Approach

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AIAA JOURNAL 2024年第11期62卷 4144-4152页

作者： Palma, Giorgio Meloni, Stefano Camussi, Roberto Iemma, Umberto Bogey, Christophe CNR Inst Marine Engn CNR INM I-00128 Rome Italy INM Rome Via Vallerano 139 Rome Italy Univ Tuscia Dept Econ Engn Soc & Business Org I-01100 Viterbo Italy Roma Tre Univ Dept Civil Comp Sci & Aeronaut Engn Via Vito Volterra 62 I-00146 Rome Italy Lab Mecan Fluides & Acoust LMFA UMR 5509 F-69130 Ecully France Univ Claude Bernard Lyon I Ecole Cent Lyon CNRS Lyon France

In this study, the parameters of a wave-packet model for subsonic jet noise prediction are systematically optimized by leveraging near- and far-field data obtained from the large-eddy simulation (LES) of a free jet at a Mach number of 0.9 across various radial distances. The utilization of near-field information is justified by the observation that the scattering surfaces are typically situated within a few nozzle diameters from the jet axis in the radial direction, both in the current and in innovative aircraft configurations. The far-field information is used to guarantee the correct subdivision between the wave-packet radiating noise and the hydrodynamic components. The results show a notable agreement between the LES data and the wave-packet solutions, consistent with findings documented in the existing literature. This agreement underscores the validity and applicability of the implemented methodology, offering an effective method for obtaining an equivalent jet noise acoustic source, easily implementable in acoustic scattering codes, and accounting for the directional behavior of jet noise.

关键词： Computational Fluid Dynamics Aircraft Noise Aircraft Components and Structure Jet Noise Aeroacoustics Turbulent-Jet Wave Packets Inverse Problems Acoustic Scattering multi-objective evolutionary algorithms Reduced Order Model

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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 97卷

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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multi-objective SCHEDULING PROBLEM FOR HYBRID MANUFACTURING SYSTEMS WITH WALKING WORKERS

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2019年第5期26卷 625-650页

作者： Yilmaz, Omer Faruk Durmusoglu, Mehmet Bulent Karadeniz Tech Univ Dept Ind Engn Trabzon Turkey Istanbul Tech Univ Dept Ind Engn Istanbul Turkey

Hybrid manufacturing systems (HMSs) are attracting attention from academia and industry owing to concerns regarding fluctuations in customers' demands. An HMS combines traditional manufacturing cells and a functional area, leading to improved flexibility in meeting customers' demands Although information on HMSs is available in the existing academic literature, scheduling problems with walking workers for such systems are rarely addressed. This study explores a multi-objective scheduling problem in HMS and proposes an optimization model to achieve three objectives (i) minimization of average flow time, (ii) reducing the maximum number of workers, and (iii) minimization of the maximum number of workers changing. The model belongs to a non-deterministic polynomial-time hardness (NP-hard) problem class, and hence, non-dominated sorting algorithm-II (NSGA-II) with a local search procedure is employed. The proposed algorithm and several widely accepted multi-objective evolutionary algorithms are compared for six different cases. A set of evaluation metrics is used to evaluate the effectiveness of the proposed algorithm in finding desirable solutions. The computational results indicate that the proposed algorithm is superior to other metaheuristics. This study contributes to existing academic literature by investigating lot scheduling problem with walking workers in the context of hybrid manufacturing systems and provides guidelines for researchers and industry practitioners.

关键词： hybrid manufacturing system scheduling problem walking workers multi-objective evolutionary algorithms NSGA-II

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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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A genetic algorithm-based framework for mining quantitative association rules without specifying minimum support and minimum confidence

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SCIENTIA IRANICA 2020年第3期27卷 1316-1332页

作者： Moslehi, F. Haeri, A. Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Discovering association rules is a useful and common technique for data mining, in which relations and co-dependencies of datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper, a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the genetic algorithm, and there is no need to determine the extent of the threshold for the support. and confidence criteria. By proposing a multi-criteria method, useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discretize numerical values and determine the minimum support threshold and minimum confidence threshold. Different criteria are considered to determine appropriate rules. In this algorithm, selected rules are extracted based on confidence, interestingness, and cosine(2). The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets, and the results show the superior performance of the proposed algorithm compared to similar algorithms. (C) 2020 Sharif University of Technology. All rights reserved.

关键词： Machine learning Data mining Quantitative association rules multi-objective evolutionary algorithms Genetic algorithm

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Set-based concept selection in multi-objective problems involving delayed decisions

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JOURNAL OF ENGINEERING DESIGN 2010年第6期21卷 619-646页

作者： Avigad, Gideon Moshaiov, Amiram ORT Braude Coll Engn Dept Mech Engn Karmiel Israel Tel Aviv Univ Dept Mech Engn IL-69978 Tel Aviv Israel

This paper introduces a computational approach to support concept selection in multi-objective design. It is motivated by: (1) a common need to delay some decisions during conceptual design due to the presence of uncertainties;and (2) intentional delay of decisions for the purpose of maintaining several optional concepts, as suggested by the concurrent engineering procedure of Toyota. Here, for the first time, a multi-objective set-based concept (SBC) selection problem with delayed decisions is formulated and solved. SBCs are conceptual solutions, which are represented by sets of particular solutions, with each concept having a one-to-many relation with the objective space. Several novel notions, such as higher-level concepts, multi-model concepts and robust concepts to delayed decisions, are defined and used. These lead to an auxiliary multi-objective decision problem. The auxiliary objectives are concept optimality and variability, both paramount to concept selection, with concept variability strongly supporting the idea of intentionally keeping several useful alternatives as long as possible. Academic and engineering examples are provided to demonstrate the proposed approach and its applicability to real-life problems. The results demonstrate that the suggested technique may well support the process of delayed decision either when needed or when deliberately done.

关键词： multi-criteria decision-making multi-objective evolutionary algorithms conceptual design uncertainties robustness design space representation

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