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Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期107卷 104531-104531页

作者： Jimenez, Fernando Sanchez, Gracia Palma, Jose Sciavicco, Guido Univ Murcia Dept Informat & Commun Engn Murcia Spain Univ Ferrara Dept Math & Comp Sci Ferrara Italy

The large amount of data that is produced today with new technologies is an impediment for machine learning algorithms to work correctly, both due to the memory requirements and the necessary execution times. That is why the processes of reducing both the quantity and the size of the data are increasingly important. One of these processes is the so-called instance selection. In this paper we propose three-objective constrained optimization models to formulate instance selection wrapper and filter methods (separately) for classification problems, which are solved with multi-objective evolutionary algorithms and multi-objective differential evolution. In the proposed instance selection wrapper method, an objective is added to the usual ones to minimize the generalization error of the classifier. The proposed instance selection filter method simultaneously optimizes the correlation, redundancy and consistency of the datasets. Instance retention constraints are imposed on optimization models to retain a maximum percentage of samples, established by the decision maker, in big data scenarios. The experiments have been designed to compare (1) the NSGA-II and MODE algorithms, (2) two- and three-objective optimization models, (3) two different constraint handling techniques, and (4) the proposed evolutionary approaches and other 12 non-evolutionary approaches used in literature. The proposed wrapper and filter instance selection methods have been used in a real-world business engineering application, and have also been validated using three public datasets to facilitate the replicability of the research results. The results of the experiments show the superiority of the three-objective constrained evolutionary techniques proposed in this paper over the non-evolutionary techniques and over the two-objective evolutionary approaches used in the literature.

关键词： multi-objective evolutionary algorithms multi-objective constrained optimization Instance selection Classification

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multi-objective parallel robotic dispensing planogram optimisation using association rule mining and evolutionary algorithms

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2018年第8期31卷 799-814页

作者： Wang, Haifeng Dauod, Husam Khader, Nourma Yoon, Sang Won Srihari, Krishnaswami SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA

This research addresses a medication planogram optimisation problem for robotic dispensing systems (RDSs) in mail-order pharmacy automation (MOPA) facilities. A MOPA is used by a high-throughput fulfilment facility that processes a large volume of prescription orders. In MOPA facilities, each RDS unit integrates auto-dispenser devices and a robot arm to count and dispense medications automatically to complete high demand. An RDS planogram is the allocation of medications in one RDS unit and their distribution in different RDS units. A significant challenge in MOPA systems is to design an efficient planogram strategy. In this study, the RDS planogram is optimised to meet three objectives: association between medications, workload balance of RDSs, and robot arm travel distance. Association rule mining (ARM) is applied to explore the associations between medications, whereas a nonlinear mixed-integer programming (MIP) model is developed to optimise medication allocation based on ARM outputs. Four evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), knee-based NSGA-II (k-NSGA-II), Pareto Archived Evolution Strategy (PAES), and Strength Pareto evolutionary Algorithm (SPEA-II), are applied to solve the proposed planogram optimisation model on eight experimental problems. Based on the different performance evaluation criteria, the best algorithm with higher performance is identified for each criterion.

关键词： Robotic dispensing planogram optimisation association rule mining multi-objective evolutionary algorithms

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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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Adaptive PBI for Massively Parallel MOEA/D in a Distributed Memory Environment

Adaptive PBI for Massively Parallel MOEA/D in a Distributed ...

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IEEE Congress on evolutionary Computation (CEC)

作者： Sato, Yuji Hirayama, Tomoya Ikami, Ryo Hosei Univ Fac Comp & Informat Sci Tokyo Japan

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

This paper proposes an adaptive PBI for massively parallel MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the reference vector in the objective function space, the T-neighbor is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a method to improve the problem that the T-neighbor is divided and the solution distribution becomes sparse by adaptively controlling the penalty value in the PBI function according to the distance from the reference vector using a distribution function such as Laplace distribution. The effectiveness of the proposed method is shown by comparison with execution using a single CPU.

关键词： MOEA/D parallel and distributed processing distributed memory environment multi-objective evolutionary algorithms

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A multi-objective differential evolution algorithm based on domination and constraint-handling switching

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INFORMATION SCIENCES 2021年 579卷 796-813页

作者： Yang, Yongkuan Liu, Jianchang Tan, Shubin Liu, Yuanchao Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Northeastern Univ Dept Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Many domination-based multi-objective evolutionary algorithms (MOEAs) are designed for constrained multi-objective optimization problems (CMOPs). However, they still face the challenge of balancing the feasibility, convergence and distribution. This paper tackles this issue by proposing a multi-objective differential evolution algorithm based on domination and a mechanism of constraint-handling switching (MODE-CHS). In constraint handling switching, if there are no feasible solutions in the population, the population evolves by constraint-handling;otherwise, the population evolves without handling constraints. This mechanism enhances the rate of population convergence to the maximum while obtaining the feasible solutions. Furthermore, in MODE-CHS, the feasible solutions are saved to an external archive and evolve together with the population to explore the feasible region. Meanwhile, to enhance the distribution, the offspring of the external archive also participates in the individual-update procedure of the population. 27 bench-mark test problems and two real-world problems are used for the performance comparison of the proposed algorithm with other five state-of-the-art algorithms. In the experiment, the proposed algorithm, MODE-CHS, is shown to produce satisfactory solutions for most of the tested functions, where other five MOEAs perform relatively worse. The experiment results demonstrate MODE-CHS is very competitive for solving CMOPs. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithms Constraint-handling technology Differential evolution Constrained multi-objective optimization problems

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Parallel Dynamic multi-objective Optimization evolutionary Algorithm 22

Parallel Dynamic Multi-Objective Optimization Evolutionary A...

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22nd International Arab Conference on Information Technology (ACIT)

作者： Grid, Maroua Belaiche, Leila Kahloul, Laid Benharzallah, Saber Biskra Univ Comp Sci Dept LINFI Lab Biskra Algeria Batna 2 Univ Batna Algeria

ISBN: (纸本)9781665419956

multi-objective optimization evolutionary algorithms (MOEAs) are considered as the most suitable heuristic methods for solving multi-objective optimization problems (MOPs). These MOEAs aim to search for a uniformly distributed, near-optimal and near-complete Pareto front for a given MOP. However, MOEAs fail to achieve their aim completely because of their fixed population size. To overcome this limit, an evolutionary approach of multi-objective optimization was proposed;the dynamic multi-objective evolutionary algorithms (DMOEAs). This paper deals with improving the user requirements (i.e., getting a set of optimal solutions in minimum computational time). Although, DMOEA has the distinction of dynamic population size, being an evolutionary algorithm means that it will certainly be characterized by long execution time. One of the main reasons for adapting parallel evolutionary algorithms (PEAs) is to obtain efficient results with an execution time much lower than the one of their sequential counterparts in order to tackle more complex problems. Thus, we propose a parallel version of DMOEA (i.e., PDMOEA). As experimental results, the proposed PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimization of computational time and converging to the desired population size.

关键词： multi-objective problems Pareto front multi-objective evolutionary algorithms Dynamic MOEA Parallel DMOEA

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Improving the Urban Accessibility of Older Pedestrians using multi-objective Optimization

Improving the Urban Accessibility of Older Pedestrians using...

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IEEE Congress on evolutionary Computation (CEC)

作者： Delgado-Enales, Inigo Molina-Costa, Patricia Osaba, Eneko Urra-Uriarte, Silvia Del Ser, Javier Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Many countries around the world have witnessed the progressive ageing of their population, giving rise to a global concern to respond to the needs that this process will create. Besides the changes in the productive schemes and the evolution of the healthcare resources to new models, the accessibility of pedestrians belonging to this age range is grasping an increasing interest in urban planning processes. This work presents preliminary results of a framework that combines graph modeling and meta-heuristic optimization to inform decision makers in urban planning when deciding how to regenerate urban spaces taking into account pedestrian accessibility for the older people in urban areas with difficult orography. The goal of the framework is to decide where to deploy urban elements (mechanical ramps, escalators and lifts), so that an indirect measure of accessibility is improved while also accounting for the economical investment of the installation. We exploit the versatility of multi-objective evolutionary algorithms to tackle the underlying optimization problem. Experimental results of a case study located in the city of Santander (Spain) show that the proposed framework can support urban planners when making decisions regarding the accessibility of the public space.

关键词： Urban planning pedestrian accessibility combinatorial optimization multi-objective evolutionary algorithms

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multi-objective evolutionary optimization of unsupervised latent variables of turning process

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： de Melo, Simone Aparecida Pereira, Robson Bruno Dutra Reis, Allexandre Fortes da Silva Lauro, Carlos Henrique Brandao, Lincoln Cardoso Univ Fed Sao Joao del Rei Sao Joao Del Rei MG Brazil Ctr Innovat Modeling & Optimizat Syst CIMOS Sao Joao Del Rei MG Brazil Ctr Innovat Sustainable Mfg CIMS Sao Joao Del Rei MG Brazil

Manufacturing process modeling and optimization is a challenging task due to the numerous objectives to be considered in the optimization. Generally, the optimization of these processes requires many objective optimization methods to deal with four or more objective functions. However, the correlation structure of the outputs cannot be disregarded. In this work, it is proposed the unsupervised learning of the outputs together with multi-objective evolutionary optimization of the turning process of AISI 4340 steel considering three scenarios varying the tool nose radius. A central composite design varying the process parameters is used to conduct the experimental tests. After tests and measurements of quality and productivity outputs the p correlated observed outputs are firstly transformed in m unobserved latent variables through factor analysis using principal axis extraction method and varimax rotation, with m < p. Subsequently, the relation between the process parameters and the scores of latent variables is modeled through response surface methodology. multi-objective evolutionary optimization methods are applied in the reduced and uncorrelated set of response models of the transformed outputs. The multi-objective algorithms are compared through hypervolume metric and the pseudo-weights approach is used to decision making. The proposed method can also be applied in other multi-response processes with correlated outputs. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Unsupervised learning Factor analysis multi-objective evolutionary algorithms Turning process

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An optimal hybrid adaptive controller based on the multi-objective evolutionary algorithm for an under-actuated nonlinear ball and wheel system

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2020年第4期34卷 1723-1734页

作者： Pezhman, Alireza Rezapour, Javad Mahmoodabadi, Mohammad Javad Islamic Azad Univ Dept Mech Engn Lahijan Branch Lahijan Iran Sirjan Univ Technol Dept Mech Engn Sirjan Iran

The present paper introduces an optimal hybrid adaptive robust sliding mode control based on a combination of feedback linearization and a PD controller (ARSMC-PD) to present a general scheme for stabilization of a ball and wheel system. Since the combination of adaptive sliding mode and feedback linearization cannot reduce the control efforts and tracking error, a PD controller is added to enhance the performance and provide sufficient optimal control input. At first, an adaptive mechanism is used to update the parameters of the SMC controller according to the gradient descent method and the chain rule of differentiation to minimize the sliding surface. Then, the strength Pareto evolutionary algorithm (SPEA-II) is utilized to minimize the control effort and the integral time absolute errors of the system response simultaneously in the presence of disturbance. The results prove the efficiency of the proposed controller in two aspects of low tracking error and optimal control input.

关键词： Sliding mode controller Feedback linearization PD controller multi-objective evolutionary algorithms Gradient descent method Ball and wheel system

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Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期293卷 615-631页

作者： Leyva Lopez, Juan Carlos Solano Noriega, Jesus Jaime Rui Figueira, Jose Liu, Jun Gastelum Chavira, Diego Alonso Univ Autonoma Occidente Dept Econ & Management Sci Culiacan 80120 Sinaloa Mexico Univ Lisbon Inst Super Tecn CEG IST Lisbon Portugal Ulster Univ Comp Sci Res Inst Newtownabbey Antrim North Ireland

ELECTRE III is a well-known multiple criteria decision aiding method based on pairwise comparisons. However, it cannot be applied to ranking problems involving many alternatives, because the number of pairwise comparisons can then be rather large. In this paper, we present an evolution-based approach for exploiting large fuzzy outranking relations and deriving a crisp outranking relation with desirable properties. Therefore, the utilization of a fuzzy outranking relation is modeled as a three-objective optimization problem, which is solved by an evolutionary algorithm. The proposed ranking algorithm is a hybrid of the elitist non-dominated sorting genetic algorithm-II (NSGA-II) and a reference point method with the repeated use of a choice mechanism. In addition, a method that portrays the obtained ranking in a Hasse diagram is used for recommendation purposes. We designate the new method RP2-NSGA-II+H. In our experiments, the proposed ranking procedure demonstrates a better performance in terms of ranking error rates than other ranking procedures based on multi-objective evolutionary algorithms. Our experimental results also demonstrate that, with the new procedure, this method can be scaled for hundreds of alternatives. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multiple criteria analysis Fuzzy outranking relations multi-objective evolutionary algorithms Ranking procedures Electre III

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