4.7 Article

Evolutionary Multitasking for Optimization Based on Generative Strategies

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Cultural transmission based multi-objective evolution strategy for evolutionary multitasking

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 Automation & Control Systems

Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignment and Adaptive Differential Evolution

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 Automation & Control Systems

Learning Task Relationships in Evolutionary Multitasking for Multiobjective Continuous Optimization

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 Automation & Control Systems

Multiobjective Evolutionary Multitasking With Two-Stage Adaptive Knowledge Transfer Based on Population Distribution

Zhengping Liang et al.

Summary: The EMT-PD algorithm improves convergence performance by adjusting search step size and dynamically changing search range based on population distribution. This two-stage adaptive knowledge transfer approach reduces negative transfer effects and enhances population diversity, helping to escape local optima. Experimental results demonstrate the superiority of EMT-PD in multitasking multiobjective optimization.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2022)

Review Computer Science, Interdisciplinary Applications

Applications of Generative Adversarial Networks (GANs): An Updated Review

Hamed Alqahtani et al.

Summary: Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data, achieving significant advancements and performance in various applications. This paper aims to summarize different variants of GANs and their potential applications in different domains.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2021)

Article Computer Science, Artificial Intelligence

Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization

Tingyang Wei et al.

Summary: Researchers have proposed a Generalized Resource Allocation (GRA) framework to dynamically allocate computational resources, enhancing the performance of multi-objective EMTO algorithms. By designing a normalized attainment function, multi-step nonlinear regression, and flexible adjustment of resource allocation intensity, the framework has shown success in various domains.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2021)

Article Thermodynamics

Multitasking multi-objective operation optimization of integrated energy system considering biogas-solar-wind renewables

Ting Wu et al.

Summary: The paper introduces a grid-connected integrated energy system that considers biogas-solar-wind complementarities and applies digester heating for biogas production. A multi-objective optimization model is used to optimize operational cost, carbon dioxide emission, and energy loss, while comparing the system to a natural gas-solar-wind IES. The study also presents an improved multitasking paradigm within the domain of multi-objective optimization to enhance convergence characteristics and performance evaluation.

ENERGY CONVERSION AND MANAGEMENT (2021)

Article Computer Science, Artificial Intelligence

Evolutionary Transfer Optimization-A New Frontier in Evolutionary Computation Research

Kay Chen Tan et al.

Summary: Evolutionary Algorithm (EA) is a nature-inspired search method that works on Darwinian principles and has been successfully applied to solve complex optimization problems. Recently, there has been growing interest in Evolutionary Transfer Optimization (ETO) which integrates knowledge learning and transfer across domains to achieve better optimization efficiency and performance. This emerging research field shows promise for developing more advanced ETO methods and applications.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2021)

Article Automation & Control Systems

Cognizant Multitasking in Multiobjective Multifactorial Evolution: MO-MFEA-II

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 Automation & Control Systems

Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation

Lei Zhou et al.

Summary: A multifactorial evolutionary algorithm (MFEA) is proposed for evolutionary multitasking, optimizing multiple tasks simultaneously with knowledge transfer. The appropriate configuration of crossover is essential for the performance of MFEA.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Automation & Control Systems

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Cheng He et al.

Summary: In recent years, there has been an increasing trend in driving evolutionary algorithms with machine learning models. The proposed multiobjective evolutionary algorithm, driven by generative adversarial networks (GANs), shows effectiveness in generating promising offspring solutions in high-dimensional decision space with limited training data. The experimental results on benchmark problems demonstrate the capability of the algorithm.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Automation & Control Systems

An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization

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)

Proceedings Paper Computer Science, Artificial Intelligence

Partial Transfer Learning for Fast Evolutionary Generative Adversarial Networks

Zheping Liu et al.

Summary: Generative Adversarial Networks (GAN) are known for generating realistic images, with Evolutionary GAN (E-GAN) being a state-of-the-art approach that requires significant computational resources. To improve efficiency, Partial Transfer training based E-GAN (PT-EGAN) is proposed, which uses smaller datasets and transfers learned features across training stages, achieving better performance on CIFAR-10 dataset with similar resources as E-GAN. PT-EGAN is effective in accelerating generative adversarial learning.

2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

EMT-ReMO: Evolutionary Multitasking for High-Dimensional Multi-Objective Optimization via Random Embedding

Yinglan Feng et al.

Summary: EMT-ReMO enhances the efficiency and effectiveness of embedding-based methods in solving high-dimensional optimization problems with low effective dimensions by embedding the target problem into multiple low-dimensional subspaces and performing implicit multi-objective evolutionary multitasking with seamless knowledge transfer. Experimental results confirm its effectiveness and efficiency on high-dimensional MOO functions.

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) (2021)

Article Computer Science, Information Systems

A Multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy

Shuangshuang Yao et al.

INFORMATION SCIENCES (2020)

Article Computer Science, Artificial Intelligence

Multiobjective Multitasking Optimization Based on Incremental Learning

Jiabin Lin et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2020)

Article Automation & Control Systems

Evolutionary Multitasking via Explicit Autoencoding

Liang Feng et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Article Automation & Control Systems

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Cuie Yang et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2019)

Article Computer Science, Artificial Intelligence

A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking

Zhengping Liang et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Computer Science, Artificial Intelligence

Evolutionary Generative Adversarial Networks

Chaoyue Wang et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Proceedings Paper Engineering, Electrical & Electronic

A multi-objective multi-factorial evolutionary algorithm with reference-point-based approach

Huynh Thi Thanh Binh et al.

2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

A Fast Memetic Multi-objective Differential Evolution for Multi-tasking Optimization

Yongliang Chen et al.

2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2018)

Article Automation & Control Systems

Multiobjective Multifactorial Optimization in Evolutionary Multitasking

Abhishek Gupta et al.

IEEE TRANSACTIONS ON CYBERNETICS (2017)

Article Computer Science, Artificial Intelligence

Multifactorial Evolution: Toward Evolutionary Multitasking

Abhishek Gupta et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2016)

Review Computer Science, Artificial Intelligence

Performance assessment of multiobjective optimizers: An analysis and review

E Zitzler et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2003)

Article Computer Science, Artificial Intelligence

A fast and elitist multiobjective genetic algorithm: NSGA-II

K Deb et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2002)