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Computer Science, Artificial Intelligence
Hongyan Chen et al.
Summary: Multiobjective multitask optimization (MMO) aims to solve multiple problems simultaneously. This study proposes an MMO algorithm that uses transfer rank and a KNN model to achieve this goal. The algorithm introduces the concept of transfer rank to quantify the priority of transfer solutions and improve the likelihood of positive results. Solutions are sorted based on transfer rank, with higher-ranked solutions assumed to be more suitable for transfer. The algorithm also prioritizes previous and positive-transfer solutions and uses a KNN model classifier to distinguish solutions with the same transfer rank. Experimental results demonstrate that the proposed algorithm is more effective than other conventional MMO techniques.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Chao Lyu et al.
Summary: This article proposes a novel algorithm for community detection in multiplex networks. The algorithm decomposes the problem into two parts, detecting specific community partitions for each component layer and finding the composite community structure shared by all layers. Experimental results demonstrate that the algorithm outperforms classical and state-of-the-art algorithms in community detection on multiplex networks.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Genghui Li et al.
Summary: This article introduces a special multitasking optimization problem, called the competitive MTOP (CMTOP), where all tasks' objectives are comparable and the optimal solution is the best among all individual problems. An evolutionary algorithm with online resource allocation strategy and adaptive information transfer mechanism is proposed to solve the CMTOP. Experimental results on benchmark and real-world problems demonstrate the effectiveness and efficiency of the proposed algorithm.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Zhiwei Xu et al.
Summary: This paper presents a novel multi-objective evolution strategy based on cultural transmission theory for solving multi-objective multi-task optimization problems. The proposed algorithm utilizes elite-guided variation strategy and horizontal cultural transmission strategy to improve convergence efficiency, and introduces an adaptive information transfer strategy to address negative transfer. Comprehensive experimental results demonstrate that the algorithm outperforms previous state-of-the-art multi-objective EMT algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Abhishek Gupta et al.
Summary: Evolutionary multitasking (EMT) is a concept that fills the potential gap of skill transfer between distinct optimization problems in evolutionary computation, by utilizing a population's implicit parallelism to jointly solve a set of tasks. This paper reviews various application-oriented explorations of EMT and provides recipes on how general problem formulations can be transformed in the light of EMT.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2022)
Article
Computer Science, Artificial Intelligence
Chao Wang et al.
Summary: MTEA-AD is a multitask evolutionary algorithm that learns inter-task relationships through anomaly detection models and transfers effective knowledge across tasks. It can adaptively adjust the degree of knowledge transfer and effectively reduce the risk of negative transfer through fair competition.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Qunjian Chen et al.
Summary: This paper proposes a new multi-objective evolutionary multi-task optimization (EMTO) algorithm by introducing cross-dimensional variable search and prediction-based individual search for efficient knowledge transfer. The algorithm is tested on benchmark problems and the experimental results demonstrate its effectiveness and efficiency.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Zhiwei Xu et al.
Summary: In this research, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve multi-objective multi-task optimization problems. The algorithm improves convergence and diversity, and reduces negative information transfer through the information molecule concentration vector.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Zhengping Liang et al.
Summary: Evolutionary multitasking (EMT) operates in the search space of multiple optimization tasks simultaneously, enhancing task-solving abilities through knowledge sharing. A novel multiobjective EMT algorithm called MOMFEA-SADE, based on subspace alignment and self-adaptive differential evolution, demonstrates superior performance in experimental results and won a competition within IEEE 2019 Congress on Evolutionary Computation.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Ziyu Hu et al.
Summary: Multitasking optimization (MTO) is a new research topic that aims to find a set of optimal solutions to simultaneously optimize multiple tasks by utilizing the correlations between tasks. The TCADE algorithm, based on transfer component analysis (TCA) and differential evolution (DE), effectively promotes knowledge transfer and achieves a higher efficiency in knowledge transfer.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Xiaoming Xue et al.
Summary: Evolutionary multitasking (EMT) is a new research topic that aims to improve convergence across multiple optimization tasks by facilitating knowledge transfer. Existing EMT algorithms are limited to homogeneous problems, and little effort has been made to generalize EMT for solving heterogeneous problems. This article proposes a novel rank loss function to achieve superior intertask mapping and derive an analytical solution for affine transformation. The proposed technique can seamlessly integrate with EMT paradigms, and its effectiveness is demonstrated through experiments on synthetic multitasking and many-tasking benchmark problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Zefeng Chen et al.
Summary: In this article, an evolutionary multitasking algorithm with learning task relationships is proposed for multiobjective multifactorial optimization (MO-MFO). The algorithm models the decision spaces of different tasks as a joint manifold and utilizes a joint mapping matrix to transfer information across different decision spaces. Experimental results demonstrate its superior performance compared to other state-of-the-art solvers in tackling complex MO-MFO problems involving heterogeneous decision spaces.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yu Xue et al.
Summary: Feature selection is a key topic in machine learning, but unreliable results may occur when facing missing data. This study proposes a novel FS modeling approach by introducing reliability as a third objective and applying the NSGA-III algorithm to address the issue. Experimental results demonstrate the effectiveness of this three-objective model coupled with NSGA-III in solving the problem.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Jose F. Rodrigues- et al.
Summary: This study introduces an artificial neural network architecture, LIG-Doctor, based on two Minimal Gated Recurrent Unit networks, which achieved consistent improvements in prognosis prediction for patients. The results could inspire future research on similar problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Amir M. Fathollahi-Fard et al.
Summary: Online purchasing within supply chain networks is generating significant interest, with the O2O commerce emerging as a major trend. This presents challenges and opportunities for supply chain management. The proposed model in this paper introduces a dual-channel, multi-product, multi-period, multi-echelon closed-loop SCND under uncertainty using a fuzzy approach.
ADVANCED ENGINEERING INFORMATICS
(2021)
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Computer Science, Information Systems
Maxim A. Dulebenets
Summary: This study introduces a new Adaptive Polyploid Memetic Algorithm (APMA) for scheduling CDT trucks in supply chains. The algorithm relies on the polyploidy concept and problem-specific hybridization techniques to improve solution quality and reduce total truck service costs.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Junayed Pasha et al.
Summary: Maritime transportation flows and container demand have been increasing, and one common strategy adopted by shipping lines is deploying large ships. However, there has been limited research on heterogeneous fleets in tactical liner shipping decisions. This study proposes an integrated optimization model to address all major tactical liner shipping decisions, including the deployment of a heterogeneous ship fleet and considering emissions generated throughout operations.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Gaurav Dhiman et al.
Summary: The study introduces the Multi-objective Seagull Optimization Algorithm (MOSOA) by extending the previously developed Seagull Optimization Algorithm (SOA). The algorithm utilizes a dynamic archive to cache non-dominated Pareto optimal solutions and employs a roulette wheel selection approach. Testing with benchmark functions shows its superiority over existing metaheuristic algorithms, especially in high convergence Pareto optimal solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
German Gonzalez-Almagro et al.
Summary: This study focuses on the constrained clustering problem and proposes a memetic elitist multiobjective evolutionary algorithm that integrates classic multiobjective optimization and single-objective optimization. Experimental results demonstrate consistent improvements in clustering and multiobjective optimization measures in favor of the proposed algorithm over state-of-the-art methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Phan Thi Hong Hanh et al.
Summary: This paper proposes two different approaches to enhance the performance of the search process for the Clustered Shortest-Path Tree Problem, one by narrowing down the search space to find the optimal solution, and the other by decomposing the multi-graph into a set of simple graphs corresponding to mutually exclusive search spaces. These methods show promising results in experiments, especially the potential of multi-tasking schemes in improving algorithm performance.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Kavitesh Kumar Bali et al.
Summary: Humans are adept at identifying recurrent patterns in diverse situations, while AI systems strive to mimic such cognitive behavior. Evolutionary multitasking is explored as an effective means of solving multiple optimization tasks simultaneously, yet there is a known limitation in the inability to adapt transfer extent in a principled way.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Tansel Dokeroglu et al.
Summary: The Harris' Hawks Optimization (HHO) is a recent metaheuristic inspired by the cooperative behavior of hawks, which simulates their hunting patterns. A new multiobjective HHO algorithm is proposed in this study to address binary classification problems and reduce the number of selected features.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Jiabin Lin et al.
Summary: Multiobjective multitasking optimization (MTO) is a novel research topic in the field of evolutionary computation, aiming to solve multiple related multiobjective optimization problems simultaneously using evolutionary algorithms. The key lies in the knowledge transfer based on sharing solutions across tasks. This study proposes a new algorithm to address MTO problems and validates its effectiveness through numerical studies on benchmark problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Zhi-Zhong Liu et al.
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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2020)
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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2020)
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(2019)
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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
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IEEE TRANSACTIONS ON NEURAL NETWORKS
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D Büche et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
(2002)
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K Deb et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
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