In order to enhance the efficacy of mobile robots, a new dung beetle optimization algorithm (NewDBO) is proposed. It is improved using four strategies to improve the search capability and convergence speed of the algo...
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To address the disadvantages such as low optimization accuracy and falling easily into local optimality when Circle Search Algorithm (CSA) solving complex high-dimensional optimization problems, an improved CSA by uti...
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Solar photovoltaic (PV) systems play a crucial role in renewable energy production. To maximize their efficiency, maximum power point tracking (MPPT) algorithms are essential. These algorithms ensure that solar PV arr...
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While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal & Zhang (2022) recently proposed a variant that replaces risk with excess *** to ...
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While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal & Zhang (2022) recently proposed a variant that replaces risk with excess *** to DRO, the new formulation-minimax excess risk optimization (MERO) has the advantage of suppressing the effect of heterogeneous noise in different ***, the choice of excess risk leads to a very challenging minimax optimization problem, and currently there exists only an inefficient algorithm for empirical *** this paper, we develop efficient stochastic approximation approaches which directly target ***, we leverage techniques from stochastic convex optimization to estimate the minimal risk of every distribution, and solve MERO as a stochastic convex-concave optimization (SCCO) problem with biased *** presence of bias makes existing theoretical guarantees of SCCO inapplicable, and fortunately, we demonstrate that the bias, caused by the estimation error of the minimal risk, is ***, MERO can still be optimized with a nearly optimal convergence ***, we investigate a practical scenario where the quantity of samples drawn from each distribution may differ, and propose a stochastic approach that delivers distribution-dependent convergence rates. Copyright 2024 by the author(s)
This paper proposes a multi-objective path planning method for UAVs based on adaptive optimization, aiming to improve the path planning efficiency and accuracy of UAVs in complex environments. This method uses an adap...
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In this paper, we extend the recently developed generalized heavy ball optimization algorithm by introducing an additional parameter. This yields an improved optimization algorithm which has a similar form to the trip...
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In order to ensure population diversity and overcome premature convergence, differential evolution strategy was introduced and a whale particle swarm hybrid algorithm was proposed. The proposed method has been achieve...
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Wireless sensor networks (WSNs) are crucial for monitoring events, but limited energy from sensor nodes reduces network lifetime. Hierarchical routing, particularly clustering, effectively manages energy consumption a...
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In this paper, we study the Multi-Objective Bi-Level optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. ...
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With the rapid development of 5G technology and the swift growth in the number of heterogeneous devices in the Electric Power Internet of Things (EP-IoT), the issue of communication resource allocation in 5G EP-IoT sy...
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