Rui Meng

Multiobjective Game Method Based on Self-Adaptive Space Division of Design Variables and Its Application to Vehicle Suspension

Based on the similarity between the game theory and the multiobjective design, the bionic mapping and the space mapping are established between the multiobjective optimization model and game model. Then, the multiobjective optimization method based on self-adaptive space division of design variables is proposed. The design variables are divided into multiple strategy subspaces and are assigned to corresponding game players by calculating impact factors, -means clustering, and correlation analysis. Strategy subspaces of game players are dynamically adjusted in the iteration process. In their own strategy subspaces, each game player takes their payoff function (the mapping of objective function) as monoobjective optimization. It gives the best strategy upon other players. And the best strategies of all players are combined into the group strategy in this game round. Triobjective optimization is carried out for vehicle suspension in this method and it is compared with the traditional game method. The results show that this method has better calculating automaticity and can effectively promote generalization of multiobjective game method and improve the computational efficiency and precision.

Multi-objective optimization of an arch dam shape under static loads using an evolutionary game method

ABSTRACT This article attempts to evaluate the safety and economic performance of an arch dam under the action of static loads. The geometric description of a crown cantilever section and the horizontal arch ring is presented. A three-objective optimization model of arch dam shape is established based on the arch dam volume, maximum principal tensile stress and total strain energy. The evolutionary game method is then applied to obtain the optimal solution. In the evolutionary game technique, a novel and more efficient exploration method of the game players’ strategy space, named the ‘sorting partition method under the threshold limit’, is presented, with the game profit functions constructed according to both competitive and cooperative behaviour. By way of example, three optimization goals have all shown improvements over the initial solutions. In particular, the evolutionary game method has potentially faster convergence. This demonstrates the preliminary proof of principle of the evolutionary game method.

A Multiobjective Game Approach with a Preferred Target Based on a Leader-Follower Decision Pattern

We propose a multiobjective leader-follower game based on the Stackelberg model, where the designer’s preferred target is taken into account. Here, the preferred target is regarded as a leader and the other targets are regarded as followers. A partition method of strategy subspace is also given. Finally, a real-life example of the multiobjective optimization design of a Chinese arch dam named “Baihetan” is presented to demonstrate the effectiveness of our proposed method.

Bi-Objective Optimization Method and Application of Mechanism Design Based on Pigs' Payoff Game Behavior

It takes two design goals as different game players and design variables are divided into strategy spaces owned by corresponding game player by calculating the impact factor and fuzzy clustering. By the analysis of behavior characteristics of two kinds of intelligent pigs, the big pigs behavior is cooperative and collective, but the small pigs behavior is noncooperative, which are endowed with corresponding game player. Two game players establish the mapping relationship between game players payoff functions and objective functions. In their own strategy space, each game player takes their payoff function as monoobjective for optimization. It gives the best strategy upon other players. All the best strategies are combined to be a game strategy set. With convergence and multiround game, the final game solution is obtained. Taking bi-objective optimization of luffing mechanism of compensative shave block, for example, the results show that the method can effectively solve bi-objective optimization problems with preferred target and the efficiency and accuracy are also well.

Finite Element Modal Analysis for Steam Turbine Blade Based on ANSYS

In response to the vibration fatigue fracture of the steam turbine blade,we construct the 3D model of cracked blade based on the actual crack location,then modal analysis is conducted to the blade with crack and one without crack using the finite element software ANSYS.Thus,we can get the respective natural frequencies and the figure of main vibration modes.The comprasion results show that the existence of the crack can make the natural frequencies of blades drop and the blades have the greatest sway and twisting deformation along the Y axis.These characteristics above can effectively identify the presence of blade cracked. It has crucial meaning for achieving cracked blade online monitoring.

Multi-Objective Optimization Method Based on WSLS Evolutionary Game Model and its Application in Mechanism Design

This paper first proposes new multi-objective optimization method based on WSLS (Win-Stay-Lose-Shift) evolutionary game model and all game players automatically adjust behavior mode according to evolution rules of WSLS game model, which improves the game method for solving multi-objective optimization problems. Besides, this paper presents solution of WSLS evolutionary game model including evolution rules, behavior mode of game players and solution steps. Finally, the effectiveness of this method is verified by solving three goals optimization problems in mechanism design.

Game study on the populational Parrondo's paradox based on BA network

In this article, the author designs a Parrondos game model of biotic population with the BA scale-free network as its spatial carrier, trying to analyze the individuals competitive and cooperative behaviour. The populational Parrondos game model includes zero-sum games among individuals and the negative sum-up games between individuals and environment. In terms of zero-sum game relations, four patterns are defined: cooperation, competition, harmony, and Matthew patterns. The simulating calculation result shows that:1)Cooperation and competition in any form is adaptive behavior. Cooperative and competitive behavior could convert the losing game combination into winning. The positive population average fitness represents the paradoxical feature that the Parrondos game is counterintuitive.2)The population average fitness of cooperation and harmony patterns based on BA network is better than that of full connectivity, whereas the average fitness of competition and Matthew patterns is worse than that. BA network is conducive to cooperation.3)The relationship of individual fitness with node degree and with clustering coefficient is disclosed. As for cooperation pattern, the greater the node degree is, the greater the individual fitness is. In regard to nodes with the same degree, the greater the clustering coefficient is, the smaller the fitness is. For the Matthew pattern, severe polarization of individual fitness turns up, and the “Butterfly Effect” shows.