Article
Computer Science, Artificial Intelligence
Biyue Li, Tong Guo, Yi Mei, Yumeng Li, Jun Chen, Yu Zhang, Ke Tang, Wenbo Du
Summary: This paper proposes a multi-objective airspace complexity mitigation model to optimize flight trajectories and ensure the safety and efficiency of air transport. The proposed Memetic Algorithm with Adaptive Local Search effectively solves the multi-objective and non-linear optimization problem. Comprehensive comparisons with other algorithms on Chinese air traffic dataset show the superiority of the proposed algorithm in reducing airspace complexity.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Mariem Belhor, Adnen El-Amraoui, Abderrazak Jemai, Francois Delmotte
Summary: This paper proposes a bi-objective mathematical model for the home health care routing and scheduling problem, and presents three solution approaches. The results of the study demonstrate the effectiveness and suitability of these approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Civil
YaPing Fu, XiaoMeng Ma, KaiZhou Gao, ZhiWu Li, HongYu Dong
Summary: This study addresses the routing and scheduling problem of service resources in elderly healthcare, proposing a multi-objective artificial bee colony algorithm with problem-specific knowledge. The results confirm the competitiveness of the proposed method in solving the problem.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Luona Wei, Lining Xing, Qian Wan, Yanjie Song, Yingwu Chen
Summary: The study introduces a multi-objective memetic approach called MOMA-TD to address the time-dependent MO-AEOSSP problem, combining MOMA with problem-specific operators. Two problem-specific crossover operators and a time-dependent local search operator are designed, with domination-based D-MOMA-TD and indicator-based I-MOMA-TD examined and compared with classical algorithms. Experimental results show that MOMA-TDs outperform comparative methods in terms of convergence, solution quality, and distribution, providing a more practical and applicable approach for the time-dependent MO-AEOSSP problem.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaomeng Ma, Yaping Fu, Kaizhou Gao, Hui Zhang, Jianhui Mou
Summary: Nowadays, population aging is a serious issue and the elderly population consumes a large amount of public medical resources. Home health care (HHC) is seen as a solution to hospitalization, helping to alleviate the pressure caused by the shortage of medical resources due to population aging. However, effectively managing and organizing HHC operations is a crucial problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Carlos O. Flor-Sanchez, Edgar O. Resendiz-Flores, Irma D. Garcia-Calvillo
Summary: This work introduces a new hybrid evolutionary algorithm called Kernel-based Hybrid Multi-Objective Optimization Algorithm (KHMO). The algorithm's main novelty lies in its updating rule based on the concept of a reproducing kernel to approximate the numerical gradient accurately. Additionally, a novel numerical strategy based on computing a normal vector is introduced for determining a searching target direction, guiding the nondominated solutions into more favorable regions. The performance of the KHMO algorithm is evaluated using IGD and HV metrics against other competitive multi-objective methods, and the results demonstrate its superior overall performance in terms of diversity, coverage, and convergence.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaomeng Ma, Yaping Fu, Kaizhou Gao, Ali Sadollah, Kai Wang
Summary: This paper addresses a multi-center, multi-objective, and stochastic routing and scheduling problem in home health care, aiming to minimize total operation and penalty costs. By developing a multi-objective cooperation evolutionary algorithm and evaluating the quality and feasibility of the obtained solutions using stochastic simulation, the performance and competitiveness of the algorithm are validated.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Jialiang Sun, Wen Yao, Tingsong Jiang, Xiaoqian Chen
Summary: Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, it becomes more difficult to evaluate the robustness of the defense model due to weak performance of existing manually designed adversarial attacks. To address this challenge, a multi-objective memetic algorithm for auto adversarial attack optimization design is proposed, which enables the automatic search for near-optimal adversarial attacks on defense models.
Article
Computer Science, Artificial Intelligence
Yacine Azouz, Dalila Boughaci
Summary: This article focuses on solving the problem of optimal web service composition using meta-heuristic approaches. Several algorithms are proposed and evaluated, demonstrating the effectiveness of the proposed multi-objective memetic algorithm in web service composition.
Article
Computer Science, Artificial Intelligence
Sezin Afsar, Juan Jose Palacios, Jorge Puente, Camino R. Vela, Ines Gonzalez-Rodriguez
Summary: In this paper, the authors study a job shop scheduling problem with the dual objectives of minimizing energy consumption during machine idle time and minimizing the project's makespan. They consider uncertainty in processing times using fuzzy numbers and propose a multi-objective optimization model along with an enhanced memetic algorithm. Experimental results validate the effectiveness of the proposed method.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper proposes a multi-objective memetic algorithm with a two-level encoding scheme to solve the energy-efficient distributed flexible flow shop scheduling problem. Through comprehensive experiments, the effectiveness of the algorithm in optimizing total weighted tardiness and energy consumption is verified.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Chengfeng Peng, Zhantao Li, Hongyang Zhong, Xiang Li, Anping Lin, Yong Liao
Summary: With the increasing automation rate of workshops and the significance of energy consumption, more and more enterprises are required not only to make scheduling decisions on production equipment but also to consider whether the scheduling of transportation equipment supports workshop production decisions. Since both workshop production scheduling and transportation scheduling are NP-hard problems, an efficient algorithm is necessary to improve workshop productivity. To solve this problem, a manufacturing-transportation multi-objective joint scheduling optimization mathematical model is established based on problem structure, production environment, and optimization objectives. The proposed algorithm incorporates a design idea of memetic algorithm (MA) and non-dominated sorting genetic algorithm-II (NSGA-II) as the basis framework, along with an effective encoding scheme, initialization method, and neighborhood search mechanism. The algorithm's parameter design is completed through variance analysis, and its advantages in solving the problem are verified by comparing and analyzing it with other algorithms in terms of hypervolume and Set Coverage (SC).
Article
Computer Science, Artificial Intelligence
Guo Li, Zexuan Zhu, Lijia Ma, Xiaoliang Ma
Summary: This paper formulates the core-periphery structure detection problem as a multi-objective optimization problem and proposes a multi-objective memetic algorithm called MOMA-PCLS, which combines evolutionary operations and plateau-climbing local search to significantly improve search efficiency. Experimental results show the superiority of MOMA-PCLS in detecting core-periphery structures in complex networks compared to other state-of-the-art algorithms.
Article
Operations Research & Management Science
Gh. Kordi, A. Divsalar, S. Emami
Summary: Health and convenience are important indicators for society, and Home Health Care (HHC) services play a crucial role in improving community health levels. However, manual nurse planning in HHC institutes often leads to inefficiency. This research presents a multi-objective mixed-integer model for home health care planning, addressing objectives such as cost, environmental emission, workload balance, and service quality. The model considers different service levels, patient preferences, and vehicle types. The epsilon-constraint method and a Multi-Objective Variable Neighborhood Search (MOVNS) are used for solving small and practical-size instances, respectively. The algorithm is evaluated using a real case study.
OPTIMIZATION LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Jiayu Liang, Yu Xue
Summary: Symbolic regression involves searching for mathematical expressions that best fit given datasets. Genetic programming is commonly used for regression, but can lead to bloating and poor generalization. This study aims to address these issues by using a multi-objective technique and incorporating mutation-based local search operators. The proposed methods outperform GP-based methods, with smaller evolved solutions and improved search ability. Among the proposed methods, MOMA_MR performs best in testing accuracy.
NEURAL PROCESSING LETTERS
(2021)
Article
Management
Liyang Xiao, Mahjoub Dridi, Amir Hajjam El Hassani, Wanlong Lin, Hongying Fei
IMA JOURNAL OF MANAGEMENT MATHEMATICS
(2019)
Meeting Abstract
Geriatrics & Gerontology
A. -A. Zulfiqar, A. Hajjam, S. Talha, M. Hajjam, J. Hajjam, S. Erve, B. Geny, E. Andres
Article
Medicine, General & Internal
F. Koehler, S. Prescher, K. Koehler
Article
Computer Science, Artificial Intelligence
Jeremy Decerle, Olivier Grunder, Amir Hajjam El Hassani, Oussama Barakat
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
Article
Operations Research & Management Science
Jeremy Decerle, Olivier Grunder, Amir Hajjam El Hassani, Oussama Barakat
Summary: Home health care structures provide care for those in need, with organizations optimizing their activities by assigning caregivers to different offices. This not only reduces travel time, but also ensures efficient service delivery.
RAIRO-OPERATIONS RESEARCH
(2021)
Article
Medicine, General & Internal
Anna Karen Garate-Escamilla, Edelmiro Garza-Padilla, Agustin Carvajal Rivera, Celina Salas-Castro, Emmanuel Andres, Amir Hajjam El Hassani
JOURNAL OF CLINICAL MEDICINE
(2020)
Article
Medicine, General & Internal
Abrar-Ahmad Zulfiqar, Orianne Vaudelle, Mohamed Hajjam, Bernard Geny, Samy Talha, Dominique Letourneau, Jawad Hajjam, Sylvie Erve, Amir Hajjam El Hassani, Emmanuel Andres
JOURNAL OF CLINICAL MEDICINE
(2020)
Article
Medicine, General & Internal
Mohamed Sraitih, Younes Jabrane, Amir Hajjam El Hassani
Summary: The article aims to design an automatic arrhythmia classification system across patients, using a new ECG database segmentation paradigm that does not require feature extraction to improve arrhythmia detection. Experimental results show that in terms of computational cost, the SVM classifier outperforms other methods, making it suitable for clinical ECG classification models.
JOURNAL OF CLINICAL MEDICINE
(2021)
Article
Health Care Sciences & Services
Abrar-Ahmad Zulfiqar, Delwende Noaga Damien Massimbo, Mohamed Hajjam, Bernard Geny, Samy Talha, Jawad Hajjam, Sylvie Erve, Amir Hajjam, Emmanuel Andres
Summary: The telemonitoring program focused on preventing geriatric syndromes in elderly COVID-19 patients was conducted during the third wave of the pandemic in France. 30 elderly patients were monitored remotely, with a mean age of 85.9 years, and significant findings related to geriatric syndromes were observed during the study.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Medicine, General & Internal
Mohamed Sraitih, Younes Jabrane, Amir Hajjam El Hassani
Summary: This study investigates an automatic ECG myocardial infarction detection system and proposes a new approach to evaluate its performance in classifying myocardial infarction under different noise types. The results show that the machine learning models used perform well in classifying myocardial infarction in the presence of different types of noise. These models can be used as detection tools for myocardial infarction in challenging environments.
JOURNAL OF CLINICAL MEDICINE
(2022)
Proceedings Paper
Engineering, Industrial
Wenheng Liu, Mahjoub Dridi, Amir Hajjam El Hassani, Hongying Fei
PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND SYSTEMS MANAGEMENT (IESM 2019)
(2019)
Proceedings Paper
Computer Science, Information Systems
Anna Karen Garate-Escamilla, Amir Hajjam El Hassani, Emmanuel Andres
PROCEEDINGS OF 2019 3RD INTERNATIONAL CONFERENCE ON CLOUD AND BIG DATA COMPUTING (ICCBDC 2019)
(2019)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
