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2009 Ninth International Conference on Hybrid Intelligent systems(第九届混合智能系统国际会议 HIS 2009)

作者： Liang Wang Zhihua Cui Jianchao Zeng Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi PR.China 030024

The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses own experience and other’s to make decision, it is easy to trap into premature convergence, but group decision making with all the individuals to make decisions uses various experiences and viewpoints to get better plan for avoiding conformity. A new formal particle swarm optimization is advanced basing on group decision(GDPSO) it takes each particle as an individual decision-maker and uses the basic information of particle such as the position of individual history and fitness value to decide a new position, then using the position replaces the global best position(pgj),So the space of searching is expanded and the population diversity is increased through the new improved algorithm, it can improve the convergence speed and the capacity of global searching, the premature convergence is avoided to some degree.

关键词： particle swarm optimization premature convergence group decision making

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Comparison and Analysis of the Selection Mechanism in the Artificial Bee Colony Algorithm

Comparison and Analysis of the Selection Mechanism in the Ar...

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2009 Ninth International Conference on Hybrid Intelligent systems(第九届混合智能系统国际会议 HIS 2009)

作者： Li Bao Jian-chao Zeng Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi PR. China 030024

Artificial bee colony (ABC) algorithm is a new global stochastic optimization algorithm based on the particular intelligent behavior of honeybee swarms, in which there exists many issues to be improved and solved. When onlooker bees exploit in ABC algorithm, they choose food source depending on the strategy of proportional selection that can result in the premature of the evolutionary process. In this paper, in order to improve the population diversity and avoid the premature, several selection strategies, such as disruptive selection strategy, tournament selection strategy and rank selection strategy, are compared and analyzed through simulation, and the results show that the modified algorithm outperforms the basic ABC algorithm.

关键词： artificial bee colony algorithm selection strategy premature convergence population diversity

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Probability Diffused Particle Swarm Optimization

Probability Diffused Particle Swarm Optimization

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2009 Ninth International Conference on Hybrid Intelligent systems(第九届混合智能系统国际会议 HIS 2009)

作者： Qiuyan Qin Zhihua Cui Jianchao Zeng Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi PR.China 030024

Premature convergence is a major problem of Particle Swarm Optimization (PSO).Although many strategies have been proposed, there is still some work needed to do in high-dimensional cases. To overcome this shortcoming, a diffused velocity update equation is designed aiming to improve the population diversity with a probability threshold. simulation results show the performance of this new variant is superior to other two previous modifications when dealing with multi-modal high-dimensional benchmark functions.

关键词： diffuse coefficient probability threshold Particle Swarm Optimization

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Individual Cognitive Parameter Setting Based on Black Stork Foraging Process

Individual Cognitive Parameter Setting Based on Black Stork ...

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2009 Ninth International Conference on Hybrid Intelligent systems(第九届混合智能系统国际会议 HIS 2009)

作者： Zhihua Cui Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi P.R.China 030024

Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stork foraging process, this paper designs a new cognitive selection strategy, in which the whole swarm is divided into adult and infant particle, and each kind particle has its special choice. simulation results show this new strategy is superior to other two previous modifications.

关键词： particle swarm optimization cognitive learning factor black stork foraging process

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Apply Inversion Order Number Genetic Algorithm to the Job Shop Scheduling Problem

Apply Inversion Order Number Genetic Algorithm to the Job Sh...

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International Conference on Genetic and Evolutionary Computing (WGEC)

作者： Xiaomei Yang Jianchao Zeng Jiye Liang Complex System & Computer Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan China College of Management Shanxi University Taiyuan China

Through analyzing the present genetic operators to solve the job shop scheduling problem, a inversion order number genetic algorithm is proposed. In view of the quality of the inversion order number, this algorithm measures the population diversity by the relative inversion order number. It uses the information provided by the inversion order number of the individual and the offspring is generated. This algorithm not only satisfies the characteristic of the job shop scheduling problem, but also develops the search capacity of genetic algorithm. The computation results validate the effectiveness of the proposed algorithm.

关键词： Genetic algorithms Job shop scheduling Scheduling algorithm Job design Algorithm design and analysis Biological system modeling Intelligent systems Laboratories Educational institutions Computer integrated manufacturing

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Apply MGA to Multi-objective Flexible Job Shop Scheduling Problem

Apply MGA to Multi-objective Flexible Job Shop Scheduling Pr...

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International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII

作者： Xiaomei Yang Jianchao Zeng Jiye Liang Complex System and Computer Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan China College of Management Shanxi University Taiyuan China

Through describing the characteristic of current genetic scheduling algorithm, a modified genetic scheduling algorithm (MGA) is proposed according to multi-objective Flexible Job Shop Scheduling Problem. This algorithm introduces a specific representation to reduce the solving space. It obtains the reasonable individuals by the selected principle and weakest link effect. Based on analyzing the benchmark of Flexible Job Shop Scheduling Problem, the computation results validate the effectivity of the proposed algorithm.

关键词： Job shop scheduling Scheduling algorithm Genetic algorithms Algorithm design and analysis Information management Innovation management Industrial engineering Intelligent systems Competitive intelligence Laboratories

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Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network system

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Defence technology（防务技术） 2009年第2期5卷 152-157页

作者：杨晨光涂序彦陈杰 Systems Engineering Research Institute China State Shipbuilding Corporation Department of Automation School of Information Science and Technology Beijing Institute of Technology Ministry of Education of Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are *** calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly *** such method,we can avoid the inconformity of the description obtained from the traditional index *** new indexes are also proposed,*** index,overlap index and cover index,which help manage the relationship among several *** normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well ***,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement *** is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

关键词：网络系统优化问题破坏概率算法改进核武器蜜蜂婚姻 Sigmoid函数

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An Extended Artificial Physics Optimization Algorithm for Global Optimization Problems

An Extended Artificial Physics Optimization Algorithm for Gl...

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International Conference on Innovative Computing, Information and Control (ICICIC)

作者： Liping Xie Jianchao Zeng Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China

Artificial Physics Optimization (APO) algorithm inspired by natural physical forces is a population-based stochastic algorithm based on Physicomimetics framework. In this paper, an extended APO (EAPO) algorithm is presented through considering the personal best positions of all individual, which can provide much useful information for search. In EAPO algorithm, the velocity updated equation is similar to that of PSO algorithm. By comparison and analysis, we can consider that EAPO algorithm is a general form of PSO algorithm and has a better diversity than PSO algorithm. The simulation results confirm that EAPO is an effective stochastic population-based search algorithm. Meanwhile, a comparison with other population-based heuristics shows that EAPO algorithm is competitive.

关键词： Educational institutions Particle swarm optimization Animals Multirobot systems Robots Physics computing Electronic mail Computational intelligence Laboratories Stochastic systems

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Using artificial physics to solve global optimization problems

Using artificial physics to solve global optimization proble...

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IEEE International Conference on Cognitive Informatics

作者： Liping Xie Jianchao Zeng Zhihua Cui Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China

Heuristics are quite an effective kind of methods to solve global optimization problems, which utilizes sample solution(s) searching the feasible regions of the problems in various intelligent ways. Inspired by physical rule, this paper proposes a stochastic global optimization algorithm based on physicomimetics framework. In the algorithm, a population of sample individuals search a global optimum in the problem space driven by virtual forces, which simulate the process of the system continually evolving from initial higher potential energy to lower one until a minimum is reached. Each individual has a mass, position and velocity. The mass of each individual corresponds to a user defined function of the value of an objective function to be optimized. An attraction-repulsion rule is constructed and used to move individuals towards the optimality. Experimental simulations show that the algorithm is effective.

关键词： Physics Computational intelligence Simulated annealing Particle swarm optimization Optimization methods Educational institutions Laboratories Electronic mail Probes Stochastic processes

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A New Modeling Reference Direct Adaptive Sliding Mode Control for Electromechanical Actuator

A New Modeling Reference Direct Adaptive Sliding Mode Contro...

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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Hao Li Lihua Dou Zhong Su Jie Chen School of Automation and the Key Laboratory of Complex System Intelligent Control and DecisionMinistry of EducationBeijing Institute of Technology School of Automation Beijing Information Science & Technology University

A new model reference direct adaptive sliding mode control(MRDASMC) approach for electromechanical actuator(EMA) is *** with parameter variation,external load disturbance,nonlinear structure and unmodelled dynamics in the system are treated as a lumped *** lumped uncertainty is estimated without the bounds known in advance.A switching function is employed to compensate for the estimated error of the lumped *** the issue of chattering in classical SMC is suppressed as the amplitude of the switching function is tuned with the tracking error by adaption *** adaption law contains a fading factor which can prevent saturation of the control *** stability of the control system is guaranteed by Lyapunov *** results show that the proposed controller can provide favorable performance and is robust to system uncertainties.

关键词： Model reference adaptive sliding mode control Electromechanical actuator Fading factor Saturation Chattering.

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