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Found 10 papers, 2 papers with code
Automated Metaheuristic Algorithm Design with Autoregressive Learning
To bridge the gap, this paper proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms.
Heuristic Solution to Joint Deployment and Beamforming Design for STAR-RIS Aided Networks
This paper tackles the deployment challenges of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) in communication systems.
Deep Reinforcement Learning Enabled Joint Deployment and Beamforming in STAR-RIS Assisted Networks
The paper constructs a STAR-RIS assisted multi-user multiple-input single-output (MU-MISO) mobile wireless network and jointly optimizes the dynamic deployment strategy of STAR-RIS and the hybrid beamforming strategy to maximize the long-term total communication rate of users.
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
In response, this paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristic optimizers.
Automated Design of Metaheuristic Algorithms: A Survey
Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality.
AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures
However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family.
Hybrid Beamforming for RIS-Aided Communications: Fitness Landscape Analysis and Niching Genetic Algorithm
To investigate the number and distribution of local optima, we conduct a fitness landscape analysis on the sum rate maximization problems.
Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic $l_0$ norm.
Multiobjective Bilevel Evolutionary Approach for Off-Grid Direction-of-Arrival Estimation
We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation.
Evolutionary Robust Clustering Over Time for Temporal Data
In many clustering scenes, data samples' attribute values change over time.
