20485137
Multi-objective scheduling problems subjected to special process constraint
The problem of parallel machine multi-objective scheduling subjected to special process constraint in the textile industries, as one of the most important combinational optimization problems, is different from other parallel machine scheduling problems in the following characteristics. On one hand, processing machines are non-identical; on the other hand, the sort of job processed on every machine can be restricted Considering one of the multi-objective problems, either minimizing the maximum completion time among all the machines(makespan) or minimizing the total earliness/tardiness penalty of all the jobs has been cornerstone of most studies done so far. However, under special process constraint, taking them into account as a multi-objective problem has not been well studied Therefore, in this paper, a multi-objective model based on them is presented and a new parallel genetic algorithm based on a vector group coding method is also proposed in order to effectively solve this model. The algorithm shows the following advantages: the coding method is simple and can effectively reflect the virtual scheduling policy, which can vividly reflect the numbers and sequences of these processed jobs on every machine, and then enables the individuals generated by crossover and mutation to satisfy process constraint. Numerical experiments show that it is efficient, and is better than the common genetic algorithm, and has the better parallel efficiency. A much better prospect of application can be optimistically expected.
19009541
Design about Real-time Fault Detection Information System of the Transformer Substation
Nowadays, the automation of our country’s power grid operation and management has reached a high level, by contrast, the automation of analysis and management of relay protection monitoring, system fault and protection action behavior seems relatively backward. This paper emphatically introduces the design of fault information system based on real-time detection platform aiming at 220kV transformer substation, including design of master station and sub-station, communication realization between master station and substation and security protection of the system and briefly clarifies the functions which the system can realize according to the characteristics of power grid.
9882029
The Group Search Optimizer and its Application on Truss Structure Design
This paper introduces a novel optimization algorithm, group search optimization (GSO) algorithm and its implementation method is presented in detail. The GSO is used to investigate the planar and space truss structures with continuous variables and is tested by two truss optimization problems. The optimization results are compared with that of the particle swarm optimization (PSO) algorithm, the particle swarm optimization with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO) algorithm. Results from the two tested cases illustrate the ability of the GSO algorithm to find the optimal results, which are better than that of the PSO and PSOPC, while are at the same level of that of HPSO optimization method. Meanwhile, the results also show that the GSO algorithm maintains a preferable convergence accuracy among these four algorithms.
5701196
An Architecture for Supporting Network Fault Recovery Management
Highly available and resilient networks play a decisive role in today's networked world. As network faults are inevitable and networks are becoming constantly intricate, finding effective fault recovery solutions in a timely manner is becoming a challenging task for administrators. Therefore, an automated mechanism to support fault resolution is essential towards efficient fault handling process. In this paper we propose an architecture to support automated fault recovery in terms of traffic engineering, recovery knowledge discovery and automated recovery planning. We base our discussion on an application scenario for recovery from border router failure to maintain optimized configuration of outbound inter-domain traffic.
14466555
Network Optimization based on Genetic Algorithm and Estimation of Distribution Algorithm
Genetic algorithm (GA) is a kind of algorithm that simulates the process and the mechanism of the evolution. Because of its unique biologic feature and its suitability to any function, it becomes very popular and has been used in many problems in many fields. Estimation of distribution algorithm (EDA) is an algorithm that is generated from the GAs. Comparing with GAs, the EDAs replace the crossover and the mutation operations in GAs with learning and sampling the probability distribution of the best individuals of the population at each iteration of the algorithm. Because of its superior, it becomes a hot topic recently. Based on the former researches, this paper mainly focuses on solving the problem of one primary network model named all-terminal network model using the strategies of the evolutionary algorithms.
33185687
Supremum of Agent Number Needed in Analyzing Security Protocols Based on Horn Logic
32866926
Lattice-Reduction-Aided MMSE Tomlinson-Harashima Precoding for MIMO Systems
In this letter, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) Tomlinson-Harashima precoding (THP) is proposed for multiple input multiple output (MIMO) systems. The extended channel is exploited to develop the LRA MMSE-THP based on the lattice reduction method. Simulation results show that the proposed scheme significantly outperforms the conventional MMSE THP and the LRA zero-forcing (ZF) THP and achieves full diversity order.