a new structure learning approach for bayesian networks (bns) based on asexual reproduction optimization (aro) is proposed in this letter. aro can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. in aro, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

the ball and beam system is one of the most popular laboratory setups for control education. in this paper, we design a fuzzy pd cascade controller for a ball and beam system using asexual reproduction optimization (aro) technique. the ball & beam system consists of a servo motor, a grooved beam, and a rolling ball. this system utilizes a servo motor to control ball’s position on the beam. chan...

We demonstrate how adjustable robust optimization (ARO) problems with fixed recourse can be casted as static robust optimization problems via Fourier-Motzkin elimination (FME). Through the lens of FME, we characterize the structures of the optimal decision rules for a broad class of ARO problems. A scheme based on a blending of classical FME and a simple Linear Programming technique that can ef...

In this paper we consider the Robust Connected Facility Location (ConFL) problem within the robust discrete optimization framework introduced by Bertsimas and Sim [7]. We propose an Approximate Robust Optimization (ARO) method that uses a heuristic and a lower bounding mechanism to rapidly find high-quality solutions. The use of a heuristic and a lower bounding mechanism—as opposed to solving t...

The artificial rabbits optimization (ARO) algorithm is proposed in this article to find the optimum values for uncertain parameters proton exchange membrane fuel cell (PEMFC) model. voltage–current polarization curve of PEMFC nonlinear, and model used paper describe it Mann’s model, which has seven parameters. sum square errors (SSE) between ARO-based estimated voltages measured defines objecti...

A novel data-driven approach for optimization under uncertainty based on multistage adaptive robust optimization (ARO) and nonparametric kernel density M-estimation is proposed. Different from conventional robust optimization methods, the proposed framework incorporates distributional information to avoid over-conservatism. Robust kernel density estimation with Hampel loss function is employed ...

In production-inventory problems customer demand is often subject to uncertainty. Therefore, it is challenging to design production plans that satisfy both demand and a set of constraints on e.g. production capacity and required inventory levels. Adjustable robust optimization (ARO) is a technique to solve these dynamic (multistage) production-inventory problems. In ARO, the decision in each st...

The artificial rabbits optimization (ARO) algorithm is a recently developed metaheuristic (MH) method motivated by the survival strategies of with bilateral symmetry in nature. Although ARO shows competitive performance compared popular MH algorithms, it still has poor convergence accuracy and problem getting stuck local solutions. In order to eliminate effects these deficiencies, this paper de...