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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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multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2002年第3期31卷 259-289页

作者： Bosman, PAN Thierens, D Univ Utrecht Inst Comp & Informat Sci NL-3508 TB Utrecht Netherlands

Stochastic optimization by learning and using probabilistic models has received an increasing amount of attention over the last few years. algorithms within this field estimate the probability distribution of a selection of the available solutions and subsequently draw more samples from the estimated probability distribution. The resulting algorithms have displayed a good performance on a wide variety of single-objective optimization problems, both for binary as well as for real-valued variables. Mixture distributions offer a powerful tool for modeling complicated dependencies between the problem variables. Moreover, they allow for elegant and parallel exploration of a multi-objective front. This parallel exploration aids the important preservation of diversity in multi-objective optimization. In this paper, we propose a new algorithm for evolutionary multi-objective optimization by learning and using probabilistic mixture distributions. We name this algorithm multi-objective Mixture-based Iterated Density Estimation evolutionary Algorithm (MIDEA). To further improve and maintain the diversity that is obtained by the mixture distribution, we use a specialized diversity preserving selection operator. We verify the effectiveness of our approach in two different problem domains and compare it with two other well-known efficient multi-objective evolutionary algorithms. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： multi-objective evolutionary algorithms probabilistic model learning numerical optimization combinatorial optimization

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Solving multi-objective Optimal Control Problems Using a multiresolution Approach

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第1期48卷 32-45页

作者： Parsonage, Ben Maddock, Christie Univ Strathclyde Dept Mech & Aerosp Engn Glasgow Scotland

This paper presents an adaptive multiresolution strategy for multi-objective optimal control problems. The optimal control problem is solved using a direct approach, with individualistic grid adaptation facilitated by a local error analysis at element boundaries. multiple objectives are considered using a dominance-based approach applying both local and global search methods to a collaborative population of unique solutions. These aspects are simultaneously incorporated via a novel application of evolutionary algorithms for adaptive optimal control problems. Together, this avoids the need for a priori specification of the quantity and temporal location of element boundaries and the set of scalarization weights defining the multi-objective descent directions. Solution fidelity can thus increase concurrently with the exploration of the design space, which leads to increased numerical efficiency while propagating and maintaining population diversity. The benefits of the proposed approach over traditional uniform-grid implementations are demonstrated. Results show that the multiresolution approach is capable of striking an effective balance between solution fidelity, population diversity, and computational cost unachievable using uniform grids.

关键词： evolutionary Algorithm Adaptive Mesh Refinement multidisciplinary Design and Optimization Optimization Algorithm Genetic Algorithm multi-objective evolutionary algorithms Optimal Control Problem

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Knowledge-guided classification and regression surrogates co-assisted multi-objective soft subspace clustering algorithm

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APPLIED INTELLIGENCE 2025年第6期55卷 1-29页

作者： Zhao, Feng Li, Lu Liu, Hanqiang Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710121 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The efficiency of multi-objective soft subspace clustering algorithms (MSSCAs) can be low when applied to large-scale datasets. This inefficiency arises because the multi-objective evolutionary algorithms (MOEAs) utilized in MSSCAs often require a large number of soft subspace clustering objective function evaluations due to their population-based nature. Moreover, relying solely on negative Shannon entropy to constrain feature weights is inadequate for soft subspace clustering algorithms. To address these issues, a knowledge-guided classification and regression surrogates co-assisted multi-objective soft subspace clustering (KCRS-MOSSC) algorithm is presented. First, an inter-cluster feature weight dissimilarity function is designed to further constrain the feature weights. Furthermore, a novel surrogate-based optimization framework called the knowledge-guided classification and regression surrogates co-assisted multi-objective evolutionary framework (KCRS-MOEF) is proposed to efficiently optimize the proposed inter-cluster feature weight dissimilarity function, intra-cluster compactness function, inter-cluster separation function, and negative Shannon entropy function. In KCRS-MOEF, a classification decision tree is utilized as the classification surrogate model to help generate a set of promising offspring, while a radial basis function (RBF) model is employed as the regression surrogate model to assist in the infill criterion by predicting the objective function values of the offspring. Furthermore, to fully leverage the knowledge of the evolutionary process, an infill criterion guided by dynamic process knowledge of elite individuals is designed to enhance the convergence and diversity of the population. Finally, a clustering ensemble strategy based on knee point guidance is proposed to generate a final solution from a set of non-dominated individuals. KCRS-MOEF outperforms state-of-the-art counterparts in terms of convergence, diversity, and time efficiency, a

关键词： Subspace clustering multi-objective evolutionary algorithms RBF Surrogate Classification decision tree Surrogate

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evolutionary algorithms for multi-objective stochastic resource availability cost problem

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OPSEARCH 2020年第3期57卷 935-985页

作者： Arjmand, Masoud Najafi, Amir Abbas Ebrahimzadeh, Majid Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran

This paper investigates the resource availability cost problem in a PERT-type network, where both activities duration and resource requirement are considered as stochastic parameters. The problem has two objective functions in which the first one, namely the project's makespan, is to minimize the project's duration. However, the second one tries to minimize the total cost of resources. Since its NP-hardness is proven in a strong sense, four well-known evolutionary algorithms including strength pareto evolution algorithm II, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and pareto envelope-based selection algorithm II are proposed to solve the problem. Furthermore, to enhance the algorithms' performance, some efficient mutation and crossover operators, as well as two novel operators called local search and movement, are employed to solution structure for producing new generations. Also, in order to tackle uncertainty, Monte-carlo simulation is utilized. In order to tune the effective parameters, the Taguchi method is used. The performance of our proposed algorithms is evaluated by numerical test problems in different size which generated based on PSPLIB benchmark problems. Finally, to assess the relative performance of the four proposed algorithms, six well-known performance criteria are employed. Using relative percentage deviation and TOPSIS approach, the performance of algorithms is elucidated.

关键词： Scheduling Resource availability cost problem multi-objective evolutionary algorithms Monte-Carlo simulation TOPSIS approach

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MoAR-CNN: multi-objective Adversarially Robust Convolutional Neural Network for SAR Image Classification

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 57-74页

作者： Wei, Hai-Nan Zeng, Guo-Qiang Lu, Kang-Di Geng, Guang-Gang Weng, Jian Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Natl Joint Engn Res Ctr Network Secur Detect & Pro Guangzhou 510632 Peoples R China Wenzhou Univ Natl Local Joint Engn Res Ctr Digitalized Elect De Wenzhou 325035 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou Peoples R China

Deep neural networks (DNNs) have been widely applied to the synthetic aperture radar (SAR) images detection and classification recently while different kinds of adversarial attacks from malicious adversary and the hidden vulnerability of DNNs may lead to serious security threats. The state-of-the-art DNNs-based SAR image detection models are designed manually by only considering the test accuracy performance on clean datasets but neglecting the models' adversarial robustness under various types of adversarial attacks. In order to obtain the best trade-off between the clean accuracy and adversarial robustness in robust convolutional neural networks (CNNs)-based SAR image classification models, this work makes the first attempt to develop a multi-objective adversarially robust CNN, called MoAR-CNN. In the MoAR-CNN, we propose a multi-objective automatic design method of the cells-based neural architectures and some critical hyperparameters such as the optimizer type and learning rate of CNNs. A Squeeze-and-Excitation (SE) layer is introduced after each cell to improve the computational efficiency and robustness. The experiments on FUSAR-Ship and OpenSARShip datasets against seven types of adversarial attacks have been implemented to demonstrate the superiority of the proposed MoAR-CNN to six classical manually designed CNNs and four robust neural architectures search methods in terms of clean accuracy, adversarial accuracy, and model size. Furthermore, we also demonstrate the advantages of using SE layer in MoAR-CNN, the transferability of MoAR-CNN, search costs, adversarial training, and the developed NSGA-II in MoAR-CNN through experiments.

关键词： Synthetic aperture radar (SAR) adversarial robustness convolutional neural networks multi-objective evolutionary algorithms hyperparameters optimization neural architectures search Synthetic aperture radar (SAR) adversarial robustness convolutional neural networks multi-objective evolutionary algorithms hyperparameters optimization neural architectures search

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Study of evolutionary algorithms for multi-objective Optimization

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SN Computer Science 2022年第5期3卷 409页

作者： Gaikwad, Rama Lakshmanan, Ramanathan School of Computer Science & Engineering Vellore Institute of Technology Vellore India

There are two prominent principles of any information retrieval system precision and analysis. Precision is the proportion of correct documents retrieved by the information retrieval system to the total number of archives retrieved in the perspective of a client's inquiry. Analysis includes the way these documents are expected to be retrieved by the client. Predicting the documents that the user might need in the upcoming time is also crucial. Having the list of multiple objectives in the user query and getting the optimum result adhering to the objectives are important these days where we can make use of evolutionary algorithms which provides set of solutions to the problem. We present here in this paper study of useful multi-objective optimization algorithms, recent developments in multi-objective evolutionary algorithms, and literature which have used these algorithms. Further, we discussed our proposed system where we will make use of the knowledge gained from this examination. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Information retrieval systems multi-objective evolutionary algorithms Query learning

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evolutionary algorithms for multi-objective optimization: Fuzzy preference aggregation and multi-sexual EAs

Evolutionary algorithms for multi-objective optimization: Fu...

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4th International Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary Computation

作者： Bonissone, SR GE Co Ctr Corp Res & Dev Schenectady NY 12301 USA

ISBN: (纸本)0819441937

There are many approaches to solving multi-objective optimization problems using evolutionary algorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for searching in multidimensional objective spaces. First we suggest the use of linguistic variables to represent preferences and the use of fuzzy rule systems to implement tradeoff aggregations. After a review of alternatives EA methods for multi-objective optimizations, we explore the use of multi-sexual genetic algorithms (MSGA). In using a MSGA, we need to modify certain parts of the GAs, namely the selection and crossover operations. The selection operator groups solutions according to their gender tag to prepare them for crossover. The crossover is modified by appending a gender tag at the end of the chromosome. We use single and double point crossovers. We determine the gender of the offspring by the amount of genetic material provided by each parent. The parent that contributed the most to the creation of a specific offspring determines the gender that the offspring will inherit. This is still a work in progress, and in the conclusion we examine many future extensions and experiments.

关键词： multi-objective evolutionary algorithms multi-sexual evolutionary algorithms preference aggregation fuzzy rule based systems

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multi-objective NETWORK RELIABILITY OPTIMIZATION USING evolutionary algorithms

MULTI-OBJECTIVE NETWORK RELIABILITY OPTIMIZATION USING EVOLU...

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15th ISSAT International Conference on Reliability and Quality in Design

作者： Aguirre, Oswaldo Villanueva, Delia Taboada, Heidi Univ Texas El Paso Dept Ind Engn El Paso TX 79968 USA

ISBN: (纸本)9780976348658

In the present paper a multi-objective evolutionary algorithm is developed to solve three different network reliability design problems taking in account reliability, cost and weight as objective functions to be optimized simultaneously. The solution presented for each of the different problems is a set of Pareto-optimal solutions. The three problems considered are the all-terminal, the k-terminal, and the two-terminal network reliability problems. To solve these well-known network reliability problems, a multiple objective evolutionary algorithm was developed. Examples are presented for each of the different problems to show the performance of the algorithm.

关键词： All-terminal k-terminal two-terminal network reliability multi-objective evolutionary algorithms

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Comparison of evolutionary multi-objective Optimization algorithms on the Tuning of PI Controllers for Electric Drives 8

Comparison of Evolutionary Multi-Objective Optimization Algo...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Dos Santos, Guilherme F. Da Silva, Wander G. Pickert, Volker De Paula, Geyverson T. School of Electrical Mechanical and Computer Engineering Federal University of Golás Goiânia Brazil School of Engineering Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798350321128

This paper presents a comparison of the two most used evolutionary algorithms for multi-objective problems, Strength Pareto evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), applied to the position control of a DC motor drive. The DC motor drive is considered with three cascade-controlled closed loops: armature current, speed and position. The outputs of the PI armature current and speed controllers are limited to the rated armature voltage and current, respectively. Because of this, higher gains for the controllers can be used, resulting in faster responses. However, windup phenomenon can arise. To avoid this, anti-windup circuits are used and, consequently, the system becomes non-linear, making the optimal tuning of the controllers even more challenging. To address this problem, a couple of multi-objective evolutionary algorithms have been employed to tune all the controllers simultaneously, together with the anti-windup window. In this paper two of them, SPEA2 and NSGA-II are investigated. They are implemented in MATLAB, while the electric drive model was developed in the SIMULINK environment. Simulation results demonstrate the algorithm's effectiveness in achieving optimal tuning for all three PI controllers while satisfying multiple conflicting objectives. Statistical tests were carried out demonstrating the advantage of SPEA2 over NSGA-II. © 2023 IEEE.

关键词： DC motor electric drives multi-objective evolutionary algorithms NSGA-II position control SPEA2

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