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Swarm collective behaviour driven by artificial physics

International Journal of Wireless and Mobile Computing 2016年第1期11卷 68-74页

作者： Xie, Liping Zeng, Jianchao Jiao, Shimin Division of Industrial and System Engineering Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Road Wanbailin District Taiyuan Shanxi030024 China

What kind of interaction and collaborative law among social creatures gives rise to swarm intelligence behaviour? Humans have cognitive limitation and therefore lack understanding of the essence of swarm intelligence. This leads to the emergence of the internal mechanism of swarm intelligence behaviour. In this paper, an artificial physics method to construct the swarm model is introduced. In the model, each individual is regarded as a physical particle. There exists virtual force among individuals, and each individual repels other individuals that are closer than R, while attracting individuals that are farther than R in distance. The law of force between any two individuals is defined and the potential field existence of the model is proved. The influence of the different values of the parameter p to swarm collective behaviour is analysed by drawing the force law curve. The simulation examples illustrate the effectiveness of the swarm model. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Swarm intelligence

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Social emotional optimisation algorithm with Levy distribution

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International Journal of Wireless and Mobile Computing 2012年第4期5卷 394-400页

作者： Cui, Zhihua Xu, Yuechun Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China State Key Laboratory of Novel Software Technology Nanjing University 210093 China

Social Emotional Optimisation Algorithm (SEOA) is a recent proposed novel population-based optimisation technique inspired by the human behaviours. In human society, the emotion guides each individual to make decision so that his/her behaviour may provide some profits. However, due to the simple setting motion in the standard version, the emotion changes can not provide enough materials. Therefore, in this paper, a stochastic model is designed by adding Levy distribution. In this model, the emotion is changed with one Levy translation. Simulation results show the proposed method is effective and efficient when dealing with multimodal famous benchmarks, especially for high-dimensional cases. Copyright © 2012 Inderscience Enterprises Ltd.

关键词： Stochastic models

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The application of particle swarm optimisation in organisational behaviour

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International Journal of Wireless and Mobile Computing 2013年第3期6卷 261-270页

作者： Shi, Zhentao Zeng, Jianchao College of Electrical and Information Engineering Lanzhou University of Technology No. 85 Langongping 730050 Lanzhou China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Road 030024 Taiyuan China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No .66 Waliu Road 030024 Taiyuan China

Based on the organisational behaviour theory and complex adaptive system method, the enterprise organisation behaviours are analysed for the firm dynamic growth evolution modelling. Further, the particle swarm optimisation algorithm is introduced to individual behaviour parameters optimising, so each individual can adjust their own behaviour parameters according to their own status and the surroundings, which makes agents more intelligent and adaptive than that in the previous research. Concretely, organisation behaviours in the firm are described at first. Then the firm growth evolution model is given and the group behaviour structure is also built up. Besides, the principles and evolution processes of the model are specifically presented and the firm growth evolution model is simulated. Finally, the simulation results are analysed in greater detail. The results illustrate that applying the particle swarm optimisation to the group behaviour has a strong guiding sense to the development of the enterprise. © 2013 Inderscience Enterprises Ltd.

关键词： Adaptive systems

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Structural optimization of Lennard-Jones clusters by hybrid social cognitive optimization algorithm

Structural optimization of Lennard-Jones clusters by hybrid ...

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IEEE International Conference on Cognitive Informatics

作者： Chen, Yongjing Cui, Zhihua Zeng, Jianchao Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China

ISBN: (纸本)9781424480401

Structural optimization of Lennard-Jones (LJ) clusters is a classical NP problem. There are many local minima locating near the global minimum, and the local optima number is increased exponentially. Social cognitive optimization. algorithm (SCOA) is a new swarm intelligent technique by simulating the human society. However, its local search capability is still weak. Therefore, in this paper, a novel local search strategy, Limited memory Broyden-Fletcher-Goldfarb- Shanno (LBFGS) is employed to enhance the exploitation capability of SCOA. Simulation results show the proposed hybrid algorithm has successfully found the lowest-energy structures of the LJ2-LJ5,LJ7-LJ9 and LJ12 comparing with PSO and the standard SCOA. © 2010 IEEE.

关键词： Structural optimization

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Group-decided particle swarm optimization

Group-decided particle swarm optimization

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10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011

作者： Yang, Hongjuan Cui, Zhihua Wang, Liang Cai, Xingjuan Tan, Ying Wu, Jianna Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China

ISBN: (纸本)9781457716966

In particle swarm optimization algorithm, computational efficiency is one key problem to affect the algorithm performance due to the dynamic balance between exploration and exploitation capabilities. In this paper, a new strategy, one group-decided position is regarded as the attraction center to provide more chances to search the local optimum. This group-decided position is estimated with all particles' fitness change ratios. Simulation results show it is effective and efficiency. © 2011 IEEE.

关键词： computational efficiency

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A new stochastic algorithm to solve Lennard-Jones clusters

A new stochastic algorithm to solve Lennard-Jones clusters

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2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011

作者： Cui, Zhihua Cai, Xingjuan Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan 030024 China

ISBN: (纸本)9781457711947

Structural optimization of Lennard-Jones clusters (LJ) plays an important role in theoretical analysis of physics and chemistry due to the exponential increased local optima. In this paper, a new evolutionary algorithm which is inspired by the plant growing process is introduced to solve this problem. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomenons. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In our algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Furthermore, one famous local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) is employed to make an efficient local search. Simulation results show this new algorithm is effective for LJ2-LJ17 when compared with standard particle swarm optimization and attractive and repulsive particle swarm optimization. © 2011 IEEE.

关键词： Structural optimization

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Using hungry particle swarm optimization to direct orbits of chaotic systems

Using hungry particle swarm optimization to direct orbits of...

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International Conference on computational Aspects of Social Networks

作者： Cai, Xingjuan Cui, Zhihua Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China

ISBN: (纸本)9780769542027

Hungry particle swarm optimization (HPSO) is a new novel variant of particle swarm optimization that incorporating the living pressure for each particle. In HPSO, total particles have three different movement motions according to different living pressures. In this paper, HPSO is employed to solve the directing orbits of chaotic systems, simulation results show this new variant increases the performance significantly when compared with the standard version of particle swarm optimization. © 2010 IEEE.

关键词： Chaotic systems

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Stochastic dynamic step length particle swarm optimization

Stochastic dynamic step length particle swarm optimization

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2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

作者： Cai, Xingjuan Cui, Zhihua Tan, Ying Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China

ISBN: (纸本)9780769538730

Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance. In this modification, each particle will adjust its velocity according to its performance. In other words, if it finds a better region, it will make a local search, otherwise, a global search pattern is given. By the way, to combining the advantages between the standard version (with better exploitation capability) and the stochastic version (with better exploration capability), the first half period is used with the standard version incorporated with dynamic step length, while in later generations, the stochastic version with dynamic step length is used to escape from a local optimum. Simulation results show this strategy may provide well balance between exploration and exploitation capabilities, and improve the performance significantly. © 2009 IEEE.

关键词： Particle swarm optimization (PSO)

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Using social cognitive optimization algorithm to solve nonlinear equations

Using social cognitive optimization algorithm to solve nonli...

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IEEE International Conference on Cognitive Informatics

作者： Cui, Zhihua Cai, Xingjuan Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China

ISBN: (纸本)9781424480401

Finding feasible solution for a nonlinear equations is a very challenging problem and generally needs a high computational efforts. In this paper, a new swarm intelligent algorithm, social cognitive optimization algorithm (SCOA), is proposed to solve this problem. In SCOA, each individual simulates one natural person. All of them are communicated through cooperation and competition to increase social status. The winner with the highest status will be the final solution. Simulation results show this new algorithm can solve the problem significantly. © 2010 IEEE.

关键词： Nonlinear equations

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Nonlinear time-varying stability analysis of particle swarm optimization

Nonlinear time-varying stability analysis of particle swarm ...

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International Conference on computational Aspects of Social Networks

作者： Fan, Weibing Cui, Zhihua Chen, Yongjing Tan, Ying Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China

ISBN: (纸本)9780769542027

As a nonlinear time-varying system, it is hard to make the stability analysis for particle swarm optimization (PSO). In this paper, geometric speed stability analysis is introduced to analyze the stability condition of PSO. According to the condition, a new inertia weight selection strategy is proposed. Simulation results show it is effective. © 2010 IEEE.

关键词： Nonlinear analysis

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