A multi-agent complex network algorithm for multi-objective optimization

Li, Xueyan; Zhang, Hankun

doi:10.1007/s10489-020-01666-8

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…For example, Brown et al presented a local computational method for the convex optimization of network structures in multi-intelligent body systems and proposed a conjugate residual estimation algorithm based on the analysis of local problems and their occurrence correlation factors and provided a theoretical basis for the application of the local computational paradigm to convex optimization problems in multi-agent systems [104]. Li et al proposed a new multi-objective multi-intelligent complex network optimization algorithm by drawing on the ideas of genetic algorithms and validated the new algorithm for seventeen unconstrained multi-objective optimization problems and seven multi-objective optimization problems [105]. Future multi-agent complex networks are evolving towards multi-scale, dynamic, and multidimensional systems where unmanned systems combined with intelligent learning algorithms play an important role in network node mobility, diffusion, and security [98,106].…”

Section: Cooperative Perception Confronts Challenges Of Mission Diver...mentioning

confidence: 99%

A Survey of Multi-Agent Cross Domain Cooperative Perception

Zhu

Wang

et al. 2022

Electronics

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Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded.

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Section: Cooperative Perception Confronts Challenges Of Mission Diver...mentioning

confidence: 99%

A Survey of Multi-Agent Cross Domain Cooperative Perception

Zhu

Wang

et al. 2022

Electronics

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“…Agents can perceive environmental information, communicate with each other and have intelligent thought and behaviour. An agent represents a solution of the problem, and multiple agents co-evolve into a multi-agent evolutionary algorithm [37].…”

Section: Chained Multi-agentmentioning

confidence: 99%

Intelligent train operation based on deep learning from excellent driver manipulation patterns

et al. 2022

IET Intelligent Trans Sys

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In the application of deep learning to realize intelligent train operation, there are some problems, such as the single learning task. Especially when using the gradient descent approach to optimize the structure, weight and threshold of a deep network, it is easy in this task to fall into a local optimum. This leads to excessive reliance on manual tuning experience. Aiming at the above issues, this paper proposes a new approach of train manipulation and prediction based on a long short-term memory (LSTM) deep network. From the perspective of automatic hyper-parameter optimization, the gradient-free intelligent search method is principally chosen to optimize the architecture and parameters of a LSTM deep network, so as to improve the manipulation accuracy based on learning from excellent drivers. This method first selects excellent driver data through the Pareto dominance principle and crowding distance calculation; on this basis, a step-by-step method is used to optimize the structure, weight and threshold of the LSTM network. Particularly, in the first step, we adopt a genetic algorithm to search for the optimal deep network structure, which overcomes the problem that the structure is difficult to determine. In the second step, we optimize the parameters of the deep network, a process that is divided into two stages of 'rough learning' and 'precise learning'. In the 'rough learning' stage, we use the multi-population chained multi-agent (MPCMA) algorithm to preliminarily optimize the LSTM network parameters. In the 'precise learning' stage, the Adam algorithm is applied to further finely optimize the network parameters. Finally, through simulation experiments, it is verified that the proposed method improves the accuracy of train manipulation and prediction, and shows strong robustness in situations of multiple manipulation sequences and different temporary speed limits.

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“…Step 1.3: Here, we introduce the method in literature [24]. Let p cut be the rewiring probability of the network, p cut ∈ [0, 1].…”

Section: Solution Algorithm Of Multi-objective Bi-level Programmingmentioning

confidence: 99%

“…Set Y = (X, P r ). According to the literature [24], let µ i ,j = Y i ,j + Y i ,j /2 and σ i ,j = Y i ,j − Y i ,j , then the crossover between Y i ,j and Y i ,j is formulated as…”

Section: Solution Algorithm Of Multi-objective Bi-level Programmingmentioning

confidence: 99%

Multi-Objective Optimization of Differentiated Urban Ring Road Bus Lines and Fares Based on Travelers’ Interactive Reinforcement Learning

Zhu

2021

Symmetry

Self Cite

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This paper proposes a new multi-objective bi-level programming model for the ring road bus lines and fare design problems. The proposed model consists of two layers: the traffic management operator and travelers. In the upper level, we propose a multi-objective bus lines and fares optimization model in which the operator’s profit and travelers’ utility are set as objective functions. In the lower level, evolutionary multi agent model of travelers’ bounded rational reinforcement learning with social interaction is introduced. A solution algorithm for the multi-objective bi-level programming is developed on the basis of the equalization algorithm of OD matrix. A numerical example based on a real case was conducted to verify the proposed models and solution algorithm. The computational results indicated that travel choice models with different degrees of rationality significantly changed the optimization results of bus lines and the differentiated fares; furthermore, the multi-objective bi-level programming in this paper can generate the solution to reduce the maximum section flow, increase the profit, and reduce travelers’ generalized travel cost.

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A multi-agent complex network algorithm for multi-objective optimization

Cited by 9 publications

References 26 publications

A Survey of Multi-Agent Cross Domain Cooperative Perception

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent train operation based on deep learning from excellent driver manipulation patterns

Multi-Objective Optimization of Differentiated Urban Ring Road Bus Lines and Fares Based on Travelers’ Interactive Reinforcement Learning

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