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In real life, people usually have a herd mentality. This paper studies the influence of herd mentality mechanism on the dynamical behaviors of rumor spreading, and proposes a novel susceptible-infected-removed (SIR) model. Based on the mean-field theory, both the critical threshold of the homogeneous networks and the final rumor size are investigated. Theoretical analysis and numerical simulation...
The dynamics of two-word naming game incorporating the influence of biased assimilation is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he will refuse to accept the conveyed word with a predefined probability, if it is different from his own current memory. Secondly,...
This paper studies mean field games for multiagent systems with multiplicative noises. By solving an auxiliary limiting optimal control problems subject to consistent mean field approximations, a set of decentralized strategies is obtained and further shown to be an asymptotical Nash equilibrium. Then, we apply the result to the consensus problem for the model of single integrators with multiplicative...
AGV path planning problems play an extremely important role in navigations of AGV. Intelligence algorithms provide an effective way to solve such complicated problems. Artificial fish swarm algorithm (AFSA) is a newly proposed promising swarm intelligence optimization algorithm, yet there still exist some disadvantages of it, such as low optimization precision and convergence rate. Aiming at these...
A new fuzzy control approach of STATCOM has been designed according to micro-genetic algorithm in this paper. Initial membership functions and control rules of FLC were fast obtained by fuzzy toolbox of MATLAB and selecting input and output observed signals, membership functions and control rules are optimized simultaneously by micro-GA. Simulation experiments for four machines power system with STATCOM...
Using the geometry theory of differential equations, the conditions of the existence of periodic solution to a prey-predator model with impulsive effects are given. The optimal control of the model is studied by optimal control principle of impulsive differential equations. The optimal harvesting time and corresponding optimal harvesting level which make people get the maximum profit are obtained...
In this study flexible job shop scheduling problem (FJSP) with interval processing times is considered and a novel shuffled frog-leaping algorithm (SFLA) is applied to minimize the maximum interval completion time. Memeplexes are constructed only once and no population shuffling is done. The cooperation of global search and neighborhood search are adopted within the search process of each memeplex...
In order to improve the performance of teaching-learning-based optimization algorithm, a hybrid teaching-learning-based optimization algorithm is presented in this paper. The hybrid approach combine with the strength of harmony search algorithm and teaching-learning-based optimization algorithm, which is aim to enhance the global search ability and local exploitation cababillity. Moreover, A new learning...
Comprehensive learning particle swarm optimization (CLPSO) algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of...
This paper aims to minimize makespan for the hybrid flowshop scheduling problem. We present a novel Fruit Fly Optimization (FFO), called multi-swarm FFO, by introducing a multiple-swarm strategy and a competition-and-updating mechanism to the basic FFO. The parameters and operators for the presented MMFO algorithm are calibrated by means of a design of experiments approach. The numerical comparisons...
Production scheduling is the key one of the basic means of production management, and the production scheduling optimization is one of the core technologies of modern management technology. According to the characteristics of quick response and order driven of production in textile machinery manufacturing enterprise, an optimal production scheduling mode is proposed which is based on improved bee...
Differential Evolution (DE) algorithm is well known as a simple and efficient scheme for multi-objective global optimization over continuous spaces. In order to reduce the calculation complexity and the diversity sorting quality, the modified non-dominated sorted differential evolution (MNSDE) algorithm is proposed in this paper. The individual distribution is large-ranging and well-proportion in...
In this paper, unrelated parallel machine scheduling problem with job rejection and earliness-tardiness penalties is investigated. The objective is to minimize the total penalty cost by deciding job acceptance, assigning jobs on unrelated machines, and determining the processing sequence of jobs on each machine. To solve this problem, a mixed integer programming (MIP) model is established, and a hybrid...
The colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem (MTSP). In CTSP, each city has one to multiple colors, allowing a salesman in the same color to visit exactly once. This work presents a dynamic CTSP (DCTSP) in which the weights of edges among the cities change over time. To solve the DCTSP, we propose a variable neighborhood search...
From simulation experiments of the multi-objective optimization control of wastewater treatment process (WWTP), it can be found that the number of obtained Pareto solutions is less using the normal non-dominated sorting genetic algorithm-II (NSGA-II) sometimes. To achieve a satisfactory optimal performance, an improved NSGA-II algorithm based on differential evolution mechanism is proposed in this...
Model predictive control (MPC) requires an explicit dynamic model to predict values of the output variable, so the accuracy of the model significantly affects the quality of control. Unfortunately, it's hard to obtain the explicit expression of unknown nonlinear systems in MPC applications. This paper describes the use of genetic programming (GP) to generate an empirical dynamic model of a process,...
As cloud computing is growing rapidly, efficient task scheduling algorithm plays a vital role to improve the resource utilization and enhance overall performance of the cloud computing environment. However, task scheduling is the severe challenge needed to solve urgently in cloud computing. Therefore, the simulated annealing multi-population genetic algorithm (SAMPGA) is proposed for task scheduling...
In order to improve the reliability and viability, advanced aircraft is equipped with abundant multiple control surfaces. Control allocation is utilized to assign the virtual control torque to these redundant control surfaces. Due to physical and aerodynamic factors, there are some constraints impacting on each control surface, which makes the control allocation problem become more complex. In this...
Condition monitoring is very important for system safety and condition-based maintenance. Time series prediction capabilities of machine learning like support vector regression (SVR) can be utilized for prognostics. But, choosing optimal parameters for SVR is an important step in SVR model design, which heavily affects the performance of SVR. So, a whale optimization algorithm (WOA) based algorithm...
Energy optimisation techniques can be suitably applied to support energy efficiency lighting retrofits in buildings. In previous studies, optimal measurement and verification (M&V) plans are developed to quantify energy savings realised by implementing large-scale lighting retrofit projects. In addition, optimal maintenance plans for the implemented energy efficiency lighting systems are also...
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