Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation

Abstract: The hybrid flowshop scheduling (HFS) problem with the objective of minimising the makespan has important applications in a variety of industrial systems. This paper presents an effective discrete artificial bee colony (DABC) algorithm that has a hybrid representation and a combination of forward decoding and backward decoding methods for solving the problem. Based on the dispatching rules, the well-known NEH heuristic, and the two decoding methods, we first provide a total of 24 heuristics. Next, an initial population is generated with a high level of quality and diversity based on the presented heuristics. A new control parameter is introduced to conduct the search of employed bees and onlooker bees with the intention of balancing the global exploration and local exploitation, and an enhanced strategy is proposed for the scout bee phase to prevent the algorithm from searching in poor regions of the solution space. A problem-specific local refinement procedure is developed to search for solution space that is unexplored by the honey bees. Afterward, the parameters and operators of the proposed DABC are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is conducted, with the best performing algorithms presented for the HFS problem under consideration, and with adaptations of some state-of-the-art metaheuristics that were originally designed for other HFS problems. The results show that the proposed DABC performs much better than the other algorithms in solving the HFS problem with the makespan criterion.

A Ferroelectric Ferromagnetic Composite Material with Significant Permeability and Permittivity

Abstract: Composite materials containing both ferroelectric and ferromagnetic phases have been synthesized from nanometer-sized powders of BaTiO3 (ferroelectric phase) and NiCuZn ferrite (ferromagnetic phase) by a standard ceramic method. The coexistence of magnetic and electric hysteresis in the composite material has been observed at room temperature. Upon the application of magnetic and electric fields, the magnetization and electric polarization of the composite material can easily be tuned based on the changing BaTiO3 content of the materials studied. These composite materials exhibit both excellent dielectric and soft-magnetic properties with a variation of the frequency. Our results strongly suggest that this composite material may be the best candidate for the development of truly integrated passive filters. Due to the combination of both inductance and capacitance in one material, the adoption of an integrated passive filter could greatly reduce the size of printed circuit boards and could efficiently suppress electromagnetic interference, thereby enabling significant miniaturization of electronic elements and devices.

A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems

Abstract: Recently, the hybridization between evolutionary algorithms and other metaheuristics has shown very good performances in many kinds of multiobjective optimization problems (MOPs), and thus has attracted considerable attentions from both academic and industrial communities. In this paper, we propose a novel hybrid multiobjective evolutionary algorithm (HMOEA) for real-valued MOPs by incorporating the concepts of personal best and global best in particle swarm optimization and multiple crossover operators to update the population. One major feature of the HMOEA is that each solution in the population maintains a nondominated archive of personal best and the update of each solution is in fact the exploration of the region between a selected personal best and a selected global best from the external archive. Before the exploration, a selfadaptive selection mechanism is developed to determine an appropriate crossover operator from several candidates so as to improve the robustness of the HMOEA for different instances of MOPs. Besides the selection of global best from the external archive, the quality of the external archive is also considered in the HMOEA through a propagating mechanism. Computational study on the biobjective and three-objective benchmark problems shows that the HMOEA is competitive or superior to previous multiobjective algorithms in the literature.

Adaptive Synchronization Between Two Different Chaotic Neural Networks With Time Delay

Abstract: This letter presents an adaptive synchronization scheme between two different kinds of delayed chaotic neural networks (NNs) with partly unknown parameters. An adaptive controller is designed to guarantee the global asymptotic synchronization of state trajectories for two different chaotic NNs with time delay. An illustrative example is given to demonstrate the effectiveness of the present method.