Search Results

In neural network models, hyperparameters have a significant impact on model performance. Currently, the commonly used hyperparameter optimization methods include manual search, grid search, random search, Bayesian optimization, and so on. However,...

The paper deals with an optimal allocation problem in a finite-source queuing system where the repair facility consists of multiple heterogeneous servers. A threshold-based allocation policy prescribes the usage of slower servers according to ...

Nature-inspired metaheuristic algorithms, like Particle Swarm Optimization (PSO), are powerful general-purpose optimization tools but they invariably do not come with rigorous theoretical justifications and can fail to find a global optimum. By ...

• We develop a novel swarm-based algorithm using the double exponential distribution.
• We show that DExPSO can converge to the global optimum and improve over PSO.
• We compare the performance of DExPSO with several swarm based ...

The cost of software testing could be reduced if faulty entities were identified prior to the testing phase, which is possible with software fault prediction (SFP). In most SFP models, machine learning (ML) methods are used, and one aspect of ...

Metaheuristic algorithms with machine learning techniques have become popular because it works so well for problems like regression, classification, rule mining, and clustering in health care. This paper’s primary purpose is to design a multi-...

This paper presents general theoretical studies on asymptotic convergence rate (ACR) for finite dimensional optimization. Given the continuous problem function and discrete time stochastic optimization process, the ACR is the optimal constant for ...$\mathrm{}$$$${\mathrm{}}^{}$

The investigation of neural network convergence represents a pivotal and indispensable area of research, as it plays a crucial role in unraveling the universal approximation capability and the intricate structural complexity inherent in these ...

Estimating copula parameters remains a challenge when dealing with multiple correlated variables. Focused studies on the application of uncommon copula functions are also still scarce. Asymmetric dependence is necessary to be taken into account as ...

Today, water supply in order to achieve sustainable development goals is one of the most important concerns and challenges in most countries. For this reason, accurate identification of areas with groundwater potential is one of the ...

• Hybrid model based on Support Vector machine & Bayesian Optimization Algorithm.

We propose and analyze a randomized zeroth-order optimization method based on approximating the exact gradient by finite differences computed in a set of orthogonal random directions that changes with each iteration. A number of previously ...

Community Detection is one of the most investigated problems as it finds application in many real-life areas. However, detecting communities and analysing community structure are very computationally expensive tasks, especially on large networks. ...

This paper focuses on mobile agents modeling biological entities such as foraging insects. It compares the behavior of randomized mobile agents with the behavior of deterministic agents subject to probabilistic perturbations of their actions ...

In learning systems, hyperparameters are parameters that are not learned but need to be set a priori. In Reservoir Computing, there are several parameters that needs to be set a priori depending on the task. Newcomers to Reservoir Computing cannot ...

SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters C and $\gamma$ to the data itself. In general, the selection of the hyperparameters is a non-convex ...$$

Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being proposed. Yet, solid comparisons with simple baselines are often missing. ...

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna ...

The common dehazing algorithms always assume that the transmission values of all the pixels in an image block are the same (local consistency assumption). However, it is easy to appear “halo” for image regions where the depth changes obviously. In ...

Multistart is a celebrated global optimization technique frequently applied in practice. In its pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of possibilities of its generalization make it very ...

We address a discrete competitive facility location problem with an asymmetric objective function and a binary customer choice rule. Both an integer linear programming formulation and a heuristic optimization algorithm based on ranking of ...

Black-box optimization aims to find the optimum of an unknown function only by evaluating it over different points in the space. An important application of black-box optimization in Machine Learning is the computationally expensive tuning of the ...