In order to address the convergence issues faced by traditional multi-objective optimization algorithms as the number of objectives increases, this paper proposes a new optimization algorithm, MaOEA-SPC, based on the ...
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Recently, the use of an unbounded external archive in the design of evolutionary multi-objective optimization (EMO) algorithms has received increasing attention. An important component in the use of an unbounded exter...
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We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. W...
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We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. We show that this random exploration approach achieves the optimal error rates. Our analysis is based on novel concentration bounds in an infinite dimensional Hilbert space established in this work, which may be of independent interest. We further develop an algorithm based on random exploration with domain shrinking and establish its order-optimal regret guarantees under both noise-free and noisy settings. In the noise-free setting, our analysis closes the existing gap in regret performance under a mild assumption on the underlying function and thereby partially resolves a COLT open problem. The proposed algorithm also enjoys a computational advantage over prevailing methods due to the random exploration that obviates the expensive optimization of a non-convex acquisition function for choosing the query points at each iteration. Copyright 2024 by the author(s)
This paper introduces a methodology for the design of IIR fractional-order digital differentiators with near-linear phase properties. The primary objective is to minimize the maximum phase deviation from the ideal lin...
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Deep neural networks are powerful and popular learning models;however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a ...
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In response to the challenges posed by the significant integration of distributed energy resources on distribution network reconfiguration, an improved particle swarm optimization algorithm is proposed in this paper. ...
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Network optimization and resiliency analysis are pivotal domains revealing network functionality, strength, and resilience. Despite their promise, these methodologies often encounter integration limitations, scalabili...
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The league chain is widely used in practical Byzantine fault tolerance consensus algorithm as the low throughput, delay higher problem. In order to solve the above problems, this paper proposes an optimization researc...
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Intrusion Detection Systems (IDS) play a pivotal role in safeguarding computer networks against malicious activities. This research explores the efficacy of two optimization algorithms, namely Whale optimization Algor...
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This paper aims to enhance the accuracy of the Pareto front estimation model as an aggregative representation of the non-dominated solutions and proposes an algorithm named the Pareto front Model optimization Algorith...
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ISBN:
(纸本)9798350308365
This paper aims to enhance the accuracy of the Pareto front estimation model as an aggregative representation of the non-dominated solutions and proposes an algorithm named the Pareto front Model optimization Algorithm (PFMOA). The typical output of multi-objective optimization is a set of non-dominated solutions approximating the Pareto front, which is the optimal trade-off between objective function values. The more non-dominated solutions there are, the more accurately the Pareto front can be approximated. However, especially in real-world problems, there is often a limitation on increasing the number of solutions due to the time required to execute objective functions. For the issue, a Pareto front estimation method interpolates between a limited number of non-dominated solutions to represent changes in objective function values even in regions where non-dominated solutions are not actually obtained. The proposed PFMOA enhances the accuracy of the Pareto front estimation model while evaluating new solutions. PFMOA generates the estimated Pareto front based on a known non-dominated solution set using Kriging. PFMOA focuses on the point with the lowest confidence levels on the estimated Pareto front and evaluates the corresponding point on the estimated Pareto set. PFMOA also generates new solutions using evolutionary variation. PFMOA stochastically switches these two model-based and evolutionary-based solution generation methods. If the new solution is non-dominated, it is included in the known non-dominated solution set, and the process is repeated to enhance the accuracy of the Pareto front estimation model. The effectiveness of the proposed PFMOA is verified using DTLZ1-3, and WFG4 problems. The results show that, in all cases, the accuracy of the Pareto front approximation by the Pareto front estimation model is higher than that by the obtained solution set itself. Additionally, the combination of model-based and evolutionary-based solution generation is bene
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