Neng-gang Xie

Path planning method for mobile robot based on ant colony optimization algorithm

A novel path planning approach based on ACO was presented aiming at mobile robots in structured environments. The information of environment constrains and path length was integrated in the fitness function which was computed by neural network, the path nodes were viewed as an ant, so with the quality of optimization of ant colony optimization algorithm, the best path was found. Finally by computer simulation, it is got that the algorithm is rational and can be used in process industry warehouse patrol measurement mobile robot real-time navigation.

A Paradoxical Evolutionary Mechanism in Stochastically Switching Environments

Organisms with environmental sensors that guide survival are considered more likely to be favored by natural selection if they possess more accurate sensors. In this paper, we develop a theoretical model which shows that under certain conditions of environmental stochasticity, selection actually favors sensors of lower accuracy. An analogy between this counter-intuitive phenomenon and the well-known Parrondo’s paradox is suggested.

Parrondo’s Games Based on Complex Networks and the Paradoxical Effect

Parrondo’s games were first constructed using a simple tossing scenario, which demonstrates the following paradoxical situation: in sequences of games, a winning expectation may be obtained by playing the games in a random order, although each game (game A or game B) in the sequence may result in losing when played individually. The available Parrondo’s games based on the spatial niche (the neighboring environment) are applied in the regular networks. The neighbors of each node are the same in the regular graphs, whereas they are different in the complex networks. Here, Parrondo’s model based on complex networks is proposed, and a structure of game B applied in arbitrary topologies is constructed. The results confirm that Parrondo’s paradox occurs. Moreover, the size of the region of the parameter space that elicits Parrondo’s paradox depends on the heterogeneity of the degree distributions of the networks. The higher heterogeneity yields a larger region of the parameter space where the strong paradox occurs. In addition, we use scale-free networks to show that the network size has no significant influence on the region of the parameter space where the strong or weak Parrondo’s paradox occurs. The region of the parameter space where the strong Parrondo’s paradox occurs reduces slightly when the average degree of the network increases.

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.

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.

A rumor spreading model based on information entropy

Rumor spreading can have a significant impact on people’s lives, distorting scientific facts and influencing political opinions. With technologies that have democratized the production and reproduction of information, the rate at which misinformation can spread has increased significantly, leading many to describe contemporary times as a ‘post-truth era’. Research into rumor spreading has primarily been based on either model of social and biological contagion, or upon models of opinion dynamics. Here we present a comprehensive model that is based on information entropy, which allows for the incorporation of considerations like the role of memory, conformity effects, differences in the subjective propensity to produce distortions, and variations in the degree of trust that people place in each other. Variations in the degree of trust are controlled by a confidence factor β, while the propensity to produce distortions is controlled by a conservation factor K. Simulations were performed using a Barabási–Albert (BA) scale-free network seeded with a single piece of information. The influence of β and K upon the temporal evolution of the system was subsequently analyzed regarding average information entropy, opinion fragmentation, and the range of rumor spread. These results can aid in decision-making to limit the spread of rumors.

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.

Evolution of cooperation driven by the diversity of emotions

Biotic individuals often compare their own payoffs with those of others, which results in endogenetic emotions ranging from compassion to harshness towards a weaker player or from respect to envy feeling against a stronger fellow. Consequently, the individual reaction can be a cooperative or defect act. Our paper establishes possible interactions between emotional characteristics and behaviour modes of individuals. For this purpose, a genetic algorithm is used to study the evolutionary process within the framework of a Prisoners Dilemma game. Our results highlight that the diversity of emotions can emerge spontaneously that is fruitful for the general cooperative act. Furthermore, it turned out that compassion is more important than respect, but both attitudes have better adaptability than harshness or envy feeling.

Flocking task research for multiple mobile robots based on game theory

It established multi-objective optimal mathematic model and did research on solving methods based on game theory. First, n robots specified n players. Second, it decomposed the design variables set and took the design variables set of paths which belonged to each robot as strategy space of the corresponding players. Third, it established mapping relationships between payoff functions and objective functions of the game. Then each player took payoff function of its own as its objective and did single-objective optimization in its own strategy space .So this player obtained the best strategy versus other players. The best strategies of all players consisted of the strategy permutation of a round game. Finally, it obtains the final game solutions (the best non-collision coordinated paths of flocking task for multiple mobile robots) through multi-round games according to the convergence criterion.

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.