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Attaining robust performance targets in data envelopment analysis with application to efficiency evaluation of airports

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Grynia, Dariusz Kadzinski, Milosz Stamenkovic, Mladen Poznan Univ Tech Fac Comp & Telecommun Piotrowo 2 PL-60965 Poznan Poland Univ Belgrade Fac Econ Kamenicka 6 Belgrade 11000 Serbia Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland

We consider a ratio -based efficiency analysis in view of achieving performance targets by Decision Making Units. The targets refer to the efficiency scores or ranks and the truth of pairwise preference relations. These outcomes can be instantiated for at least one or all feasible weights associated with inputs and outputs. We discuss Multiple Objective Optimization problems that minimize the required reductions of consumed resources or increases of produced results needed to reach the pre -defined aims. The proposed models offer great flexibility to the Decision Maker, who can indicate which factors should be modified and to which extent. The evolutionary optimization algorithm computes a set of solutions that exhibit the trade-offs between expected modifications of various factors. They can serve as the basis for interactively selecting the most preferred solution to be implemented in practice. The use of the proposed framework is illustrated in the problem of analyzing the efficiency of Polish airports.

关键词： Data envelopment analysis Multiple objective optimization Performance targets Robustness analysis evolutionary algorithms

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EEG-based motor imagery classification with quantum algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Olvera, Cynthia Ross, Oscar Montiel Rubio, Yoshio Ctr Invest & Desarrollo Tecnol Digital Inst Politecn Nacl 1310 Inst Politecn Nacl Tijuana 22430 Baja California Mexico

Developing efficient algorithms harnessing the power of current quantum processors has sparked the emergence of techniques that combine soft computing with quantum computing. This paper proposes two methods that effectively exploit quantum processors for electroencephalography-based motor imagery classification. The first method is a wrapper feature selection approach based on a quantum genetic algorithm, while the second approach employs a hybrid classical-quantum network comprising a baseline feature extraction network and a variational quantum circuit serving as the classifier. Our evaluation on the BCI Competition IV dataset 2b yielded competitive mean accuracies of 83.82%, 85.56%, and 73.73% for the subject -dependent cross -validation, subject -dependent hold -out validation, and subject -independent leaving one subject out classification approaches, respectively. Notably, our statistical analysis revealed that the hybrid models performed on par with the majority of state-of-the-art architectures, underscoring the practical viability of quantum -hybrid methodologies in real -world problem -solving scenarios.

关键词： Quantum computing EEG Deep learning evolutionary algorithms

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A cluster prediction strategy with the induced mutation for multi

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INFORMATION SCIENCES 2024年 661卷

作者： Xu, Kangyu Xia, Yizhang Zou, Juan Hou, Zhanglu Yang, Shengxiang Hu, Yaru Liu, Yuan Minist Educ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Xiangtan Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan Hunan Peoples R China Xiangtan Univ Fac Sch Comp Sci Sch Cyberspace Sci Xiangtan 411105 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Dynamic multi -objective optimization problems (DMOPs) are multi -objective optimization problems in which at least one objective and/or related parameter vary over time. The challenge of solving DMOPs is to efficiently and accurately track the true Pareto-optimal set when the environment undergoes changes. However, many existing prediction -based methods overlook the distinct individual movement directions and the available information in the objective space, leading to biased predictions and misleading the subsequent search process. To address this issue, this paper proposes a prediction method called IMDMOEA, which relies on cluster center points and induced mutation. Specifically, employing linear prediction methods based on cluster center points in the decision space enables the algorithm to rapidly capture the population's evolutionary direction and distributional shape. Additionally, to enhance the algorithm's adaptability to significant environmental changes, the induced mutation strategy corrects the population's evolutionary direction by selecting promising individuals for mutation based on the predicted result of the Pareto front in the objective space. These two complementary strategies enable the algorithm to respond faster and more effectively to environmental changes. Finally, the proposed algorithm is evaluated using the JY, dMOP, FDA, and F test suites. The experimental results demonstrate that IMDMOEA competes favorably with other state-of-the-art algorithms.

关键词： Dynamic multi-objective optimization evolutionary algorithms Multi-objective optimization problems Prediction-based reaction

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Dual therapy of cancer using optimal control supported by swarm intelligence

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BIO-MEDICAL MATERIALS AND ENGINEERING 2024年第3期35卷 249-264页

作者： Tan, Poh Ling Kanesan, Jeevan Chuah, Joon Huang Badruddin, Irfan Anjum Abdellatif, Abdallah Kamangar, Sarfaraz Hussien, Mohamed Baig, Maughal Ahmed Ali Ahammad, N. Ameer Univ Malaya Dept Elect Engn Fac Engn Kuala Lumpur Malaysia King Khalid Univ Mech Engn Dept Coll Engn Abha Saudi Arabia King Khalid Univ Dept Chem Fac Sci Abha Saudi Arabia CMR Tech Campus Dept Mech Engn Hyderabad Telangana India Univ Tabuk Dept Math Fac Sci Tabuk Saudi Arabia

BACKGROUND: The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. OBJECTIVE: To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. METHODS: Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. RESULTS: In both techniques, MOEA/ D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. CONCLUSION: This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.

关键词： Hybrid optimal control particle swarm optimization evolutionary algorithms constrained optimization multiobjective optimization

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Large-Scale WSNs Resource Scheduling Algorithm in Smart Transportation Monitoring Based on Differential Ion Coevolution and Multi-Objective Decomposition

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 829-838页

作者： Zhang, Lei Liu, Tingting Ding, Xuefeng China Three Gorges Univ Hubei Prov Engn Technol Res Ctr Construct Qual Te Yichang 443002 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China China Three Gorges Univ Sch Econ & Management Yichang 443002 Peoples R China

At present, wireless sensor networks (WSNs) play an important role in collecting and processing information in smart transportation monitoring. Inevitably, the performance of the resource scheduling algorithm directly determines the quality of service of WSNs. In this paper, we present a novel large-scale resource scheduling algorithm of WSNs based on differential ion coevolution and multi-objective decomposition (DIC-MOD) to optimize the performance of WSNs. We first introduce a certain number of mobile nodes with higher configuration into WSNs and consider them as relay nodes to strengthen the balance of energy consumption in the entire WSNs. Subsequently, we build a multi-index service quality evaluation model, including coverage, connectivity, energy efficiency and the number of nodes required to work, to characterize the comprehensive performance of WSNs. Afterward, to optimize the above complex model effectively, we propose a multi-objective resource scheduling algorithm, in which a differential ion coevolution strategy and a fast individual selection strategy based on multi-objective decomposition optimization are proposed in specific. Compared with other state-of-the-art algorithms, the experimental results finally show that the performance of WSNs on multiple indicators obtained by the proposed algorithm has been improved considerably.

关键词： Wireless sensor networks Monitoring Wireless communication Smart transportation Optimization Scheduling algorithms Relays Multi-objective optimization evolutionary algorithms wireless sensor networks resource scheduling smart transportation

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F-EvoRecSys: An Extended Framework for Personalized Well-Being Recommendations Guided by Fuzzy Inference and evolutionary Computing

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第6期24卷 2783-2797页

作者： Palomares, Ivan Alcaraz-Herrera, Hugo Shen, Kao-Yi Natl Cheng Kung Univ Tainan 70101 Taiwan Univ Bristol Comp Sci Dept Bristol BS8 1TH Avon England Chinese Culture Univ Dept Banking & Finance Taipei 111 Taiwan

People nowadays deal with busy and dynamic lifestyles on a daily basis. Adopting or maintaining a healthy lifestyle to prevent chronic conditions is therefore a core societal challenge. It is thus critical to engage and motivate citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: "good health and well-being". This is why Health Recommender Systems have recently become a research trend, particularly in the domains of food and physical activity recommendation. In this work, we present F-EvoRecSys: an extension of an evolutionary algorithm-driven solution for "healthy bundle" recommendations to help users improve their well-being. F-EvoRecSys presents the novelty of incorporating a fuzzy inference system with the aim of improving physical activity recommendations, predicated on users' exercising habit information. Through an experimental study and a live study with real participants, we demonstrate the feasibility of F-EvoRecSys to produce more diversified recommendations, while maintaing a balance between adapting to the user health needs and matching her/his individual preferences. We finally provide a discussion about challenges and future directions for personalized well-being recommender systems, under three points of view: AI and data approaches, role of fuzzy systems, and application domain considerations.

关键词： Recommender systems Personalized health evolutionary algorithms Fuzzy inference Fuzzy rule base

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Hybridization of Electromagnetism with Multi-objective evolutionary algorithms for RCPSP 14

Hybridization of Electromagnetism with Multi-objective Evolu...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Xiao, Jing Wu, Zhou Tang, Jian-Chao S China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China

ISBN: (纸本)9781450326629

As one of the most challenging combinatorial optimization problems in scheduling, the resource-constrained project scheduling problem (RCPSP) has attracted numerous scholars' interest resulting in considerable research in the past few decades. However, most of these papers focused on the single objective RCPSP;only a few papers concentrated on the multi-objective resource-constrained project scheduling problem (MORCPSP). Inspired by a procedure called electromagnetism (EM), which can help a generic populationbased evolutionary search algorithm to obtain good results for single objective RCPSP, in this paper we attempt to extend EM and hybridize it with three reputable state-of-theart multi-objective evolutionary algorithms (M0EAs) i.e. NSGA-II, SPEA2 and MOEA/D, for MORCPSP. Our two objectives are minimizing makespan and total tardiness. We perform computational experiments on standard benchmark datasets. Empirical comparison and analysis of the results obtained by the hybridization versions of EM with NSGA-II, SPEA2 and MOEA/D are conducted. The results demonstrate that EM can improve the performance of NSGA-II and SPEA2.

关键词： Resource-constrained project scheduling Multi-objective optimization Electromagnetism evolutionary algorithms

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Darwin, Lamarck, or Baldwin: Applying evolutionary algorithms to Machine Learning Techniques

Darwin, Lamarck, or Baldwin: Applying Evolutionary Algorithm...

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IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (WI-IAT)

作者： Holzinger, Andreas Blanchard, David Bloice, Marcus Holzinger, Katharina Palade, Vasile Rabadan, Raul Inst Med Informat Stat & Documentat Res Unit HCI Graz Austria Univ Oxford Dept Comp Sci Oxford OX1 2JD England Coventry Univ Dept Comp Coventry W Midlands England Columbia Univ Columbia Med Ctr Dept Biomed Informat New York NY USA

ISBN: (纸本)9781479941438

evolutionary algorithms (EAs), inspired by biological mechanisms observed in nature, such as selection and genetic changes, have much potential to find the best solution for a given optimisation problem. Contrary to Darwin, and according to Lamarck and Baldwin, organisms in natural systems learn to adapt over their lifetime and allow to adjust over generations. Whereas earlier research was rather reserved, more recent research underpinned by the work of Lamarck and Baldwin, finds that these theories have much potential, particularly in upcoming fields such as epigenetics. In this paper, we report on some experiments with different evolutionary algorithms with the purpose to improve the accuracy of data mining methods. We explore whether and to what extent an optimisation goal can be reached through a calculation of certain parameters or attribute weightings by use of such evolutionary strategies. We provide a look at different EAs inspired by the theories of Darwin, Lamarck, and Baldwin, as well as the problem solving methods of certain species. In this paper we demonstrate that the modification of well-established machine learning techniques can be achieved in order to include methods from genetic algorithm theory without extensive programming effort. Our results pave the way for much further research at the cross section of machine learning optimisation techniques and evolutionary algorithm research.

关键词： data mining evolutionary computation learning (artificial intelligence) problem solving Baldwin theory Darwin theory EA Lamarck theory biological mechanisms evolutionary algorithms machine learning natural systems optimisation Biological cells Evolution (biology) evolutionary computation Genetic algorithms Genetics Machine learning algorithms Optimization Data Mining evolutionary algorithms Machine Learning evolutionary algorithm data mining Problem solving Artificial Intelligence Machine Learning Machine learning algorithms cytology Genetic Algorithm (GA) Policies and plans Genetics

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Kolmogorov-Arnold networks guided whale optimization algorithm for feature selection in medical datasets

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JOURNAL OF BIG DATA 2025年第1期12卷

作者： Zheng, Boli Chen, Yi Wang, Chaofan Heidari, Ali Asghar Liu, Lei Chen, Huiling Liang, Guoxi Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Feature selection (FS), dealing with pathological data with a high dimensionality and a small number of samples, has always been quite challenging. Among these, wrapper-based FS methods utilizing evolutionary algorithms have gained immense popularity due to their high accuracy. Additionally, utilizing surrogate models alongside wrapper-based FS methods can help save time by reducing the need for real model predictions. However, the use of surrogate models alongside evolutionary algorithms (EAs) presents two major challenges. Firstly, the training samples for the surrogate model all come from the iterative process of EAs. For surrogate models, the limited number of iterations in EAs results in a sparse amount of training samples. Additionally, FS emphasizes reducing feature quantity, which further contributes to the sparsity of the training data. In this paper, we propose a method for constructing training data based on the reliefF algorithm, which not only enables the acquisition of a large amount of training data but also helps in addressing the issue of feature sparsity. Furthermore, we propose the use of Kolmogorov-Arnold networks (KAN) as surrogate models to address the issue of sparse features. Finally, the whale optimization algorithm (WOA) was chosen as the evolutionary algorithm, as it has exhibited excellent performance in FS problems. Experimental results indicate that using KAN as a surrogate model to identify superior individuals generated by the WOA is feasible and has shown excellent performance in addressing medical FS problems.

关键词： Feature selection Whale optimization algorithm Kolmogorov-Arnold networks Surrogate models evolutionary algorithms

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Bio-inspired algorithms for cybersecurity - a review of the state-of-the-art and challenges

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第1期23卷 1-15页

作者： Chui, Kwok Tai Liu, Ryan Wen Zhao, Mingbo Zhang, Xinyu Hong Kong Metropolitan Univ Sch Sci & Technol Dept Elect Engn & Comp Sci Hong Kong Peoples R China Wuhan Univ Technol Sch Nav Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China Donghua Univ Sch Informat Sci & Technol Shanghai 200051 Peoples R China Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

It is witnessed that the popularity of the research in cybersecurity using bio-inspired algorithms (a key subset of natural algorithms) is ever-growing. As an emergent research area, researchers have devoted efforts to applying and comparing various bio-inspired algorithms to cybersecurity applications. It is necessary to have a systematic review of bio-inspired algorithms for cybersecurity to fill the gap in the missing research study on this topic. The research contributions of this review article are four-fold. It first highlights the foundation of the baseline and latest development of 12 popular bio-inspired algorithms in three categories namely ecology-based, evolutionary-based and swarm intelligence-based algorithms. A systematic review is conducted to synthesise and compare the research methodologies, results and limitations. In-depth discussion will be made on the shortlisted and highly cited articles. The tips to select appropriate algorithm or the combination of multiple algorithms have been reported, along with the pros and cons on the design and formulations. Future research directions will be presented to meet the trends and unexplored research.

关键词： bio-inspired algorithms cybersecurity ecology-based algorithm machine learning evolutionary algorithms multi-objective optimisation swarm intelligence trade-off solution

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