A Network-Based Deterministic Model for Causal Complexity

Despite the widespread use of techniques and tools for causal analysis, existing methodologies still fall short as they largely regard causal variables as independent elements, thereby failing to appreciate the significance of the interactions of causal variables. The prospect of inferring causal relationships from weaker structural assumptions compels for further research in this area. This study explores the effects of the interactions of variables in the context of causal analysis, and introduces new advancements to this area of research. In this study, we introduce a new approach for the causal complexity with the goal of making the solution set closer to deterministic by taking into consideration the underlying patterns embedded within a dataset; in particular, the interactions of causal variables. Our model follows the configurational approach, and as such, is able to account for the three major phenomena of conjunctural causation, equifinality, and causal asymmetry

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

The formation of blue large-amplitude pulsators from white-dwarf main-sequence star mergers

Blue large-amplitude pulsators (BLAPs) are hot low-mass stars which show large-amplitude light variations likely due to radial oscillations driven by iron-group opacities. Period changes provide evidence of both secular contraction and expansion amongst the class. Various formation histories have been proposed, but none are completely satisfactory. \citet{Zhang2017} proposed that the merger of a helium core white dwarf with a low-mass main-sequence star (HeWD+MS) can lead to the formation of some classes of hot subdwarf. We have analyzed these HeWD+MS merger models in more detail. Between helium-shell ignition and full helium-core burning, the models pass through the volume of luminosity -- gravity-- temperature space occupied by BLAPs. Periods of expansion and contraction associated with helium-shell flashes can account for the observed rates of period change. We argue that the HeWD+MS merger model provides at least one BLAP formation channel.Comment: 13 pages, 8 figures, accepted by Ap

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

Networked Supervisor Synthesis Against Lossy Channels with Bounded Network Delays as Non-Networked Synthesis

In this work, we study the problem of supervisory control of networked discrete event systems. We consider lossy communication channels with bounded network delays, for both the control channel and the observation channel. By a model transformation, we transform the networked supervisor synthesis problem into the classical (non-networked) supervisor synthesis problem (for non-deterministic plants), such that the existing supervisor synthesis tools can be used for synthesizing networked supervisors. In particular, we can use the (state-based) normality property for the synthesis of the supremal networked supervisors, whose existence is guaranteed by construction due to our consideration of command non-deterministic supervisors. The effectiveness of our approach is illustrated on a mini-guideway example that is adapted from the literature, for which the supremal networked supervisor has been synthesized in the synthesis tools SuSyNA and TCT.Comment: This paper is under review for Automatic