1.

A Hybrid Genetic Algorithm for Vehicle Routing Problem with Complex Constraints





CHEN Yan LU Jun LI Zengzhi《国际设备工程与管理》,2006年第11卷第2期


Most research on the Vehicle Routing Problem （VRP） is focused on standard conditions, which is not suitable for specific cases. A Hybrid Genetic Algorithm is proposed to solve a Vehicle Routing Problem （VRP） with complex side constraints. A novel coding method is designed especially for side constraints. A greedy algorithm combined with a random algorithm is introduced to enable the diversity of the initial population, as well as a local optimization algorithm employed to improve the searching efficiency. In order to evaluate the performance, this mechanism has been implemented in an oil distribution center, the experimental and executing results show that the near global optimal solution can be easily and quickly obtained by this method, and the solution is definitely satisfactory in the VRP application.

2.

Application of Rollout Strategy to Test Points Selection for IntegerCoded Fault Wise Table





ChengLin Yang ShuLin Tian Bing Long《中国电子科技》,2009年第7卷第4期


Test points selection for integercoded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depthfirst graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rolloutbased test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integercoded fault wise tables. Computa tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.

3.

Automatic pipes routing based on flow loss in electromechanical product





付宜利 封海波 李荣 马玉林《哈尔滨工业大学学报(英文版)》,2009年第16卷第5期


Based on flow loss,a new automatic piperouting algorithm is proposed for electromechanical product in 3D space,which consists of preprocessing and optimization search.Utilizing chaos theory,a chaos grid preprocessing model (CGPM) is established to efficiently pick up the solution space and reduce the search range in the preprocessing,which simplifies the optimization search.A modified particle swarm optimization (PSO) algorithm is presented to seek for an approximate optimal trajectory in the solution space in the optimization search based on standard PSO algorithm and migration characters of people.The comparison of experiments and analysis results shows that the modified PSO algorithm is capable of preventing prematurity effectively and searching for the optimal trajectory more efficiently.Theoretical analysis proves that the modified PSO algorithm converges at global optimum.The examples show that the automatic piperouting algorithm based on flow loss is effective and practical for electromechanical product.

4.

A hybrid multiobjective PSO algorithm with local search strategy for VLSI partitioning





Wenzhong GUO Genggeng LIU Guolong CHEN Shaojun PENG《Frontiers of Computer Science in China》,2014年第2期


Very large scale integration （VLSI） circuit par titioning is an important problem in design automation of VLSI chips and multichip systems; it is an NPhard combi national optimization problem. In this paper, an effective hy brid multiobjective partitioning algorithm, based on discrete particle swarm optimzation （DPSO） with local search strat egy, called MDPSOLS, is presented to solve the VLSI two way partitioning with simultaneous cutsize and circuit delay minimization. Inspired by the physics of genetic algorithm, uniform crossover and random twopoint exchange operators are designed to avoid the case of generating infeasible so lutions. Furthermore, the phenotype sharing function of the objective space is applied to circuit partitioning to obtain a better approximation of a true Pareto front, and the theorem of Markov chains is used to prove global convergence. To improve the ability of local exploration, FiducciaMatteyses （FM） strategy is also applied to further improve the cutsize of each particle, and a local search strategy for improving circuit delay objective is also designed. Experiments on IS CAS89 benchmark circuits show that the proposed algorithm is efficient.

5.

Global optimization method using SLE and adaptive RBF based on fuzzy clustering 被引次数：1





Huaguang Zhu Li Liu Teng Long Junfeng Zhao《机械工程学报(英文版)》,2012年第25卷第4期


High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function(RBF) based on fuzzy clustering(ARFC) is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration(SLE) is employed to obtain sample points with good performance in both spacefilling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy cmeans clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method(ARSM) proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or nearglobal design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.

6.

Fuzzy controller based on chaos optimal design and its application 被引次数：2





邹恩 李祥飞 张泰山《中南工业大学学报(英文版)》,2004年第11卷第1期


In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrierwave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.

7.

Research on cultural algorithm for solving routing problem of mobile agent





MA Jun ZHANG Jianpei YANG Jing CHENG Lili《中国邮电高校学报(英文版)》,2008年第15卷第4期


The key idea behind cultural algorithm is to explicitly acquire problemsolving knowledge from the evolving population and in return apply that knowledge to guide the search. In this article, cultural algorithmsimulated annealing is proposed to solve the routing problem of mobile agent. The optimal individual is accepted to improve the belief space＇s evolution of cultural algorithms by simulated annealing. The step size in search is used as situational knowledge to guide the search of optimal solution in the population space. Because of this feature, the search time is reduced. Experimental results show that the algorithm proposed in this article can ensure the quality of optimal solutions, and also has better convergence speed. The operation efficiency of the system is considerably improved.

8.

Optimization Strategy Using Dynamic Metamodel Based on Trust Region and Biased Sampling Method





Jianqiao Yu Fangzheng Chen Yuanchuan Shen《北京理工大学学报(英文版)》,2019年第28卷第2期


Combining a trust region method with a biased sampling method, a novel optimization strategy (TRBSKRG) based on a dynamic metamodel is proposed. Initial sampling points are selected by a maximin Latin hypercube design method, and the metamodel is constructed with Kriging functions. The global optimization algorithm is employed to perform the biased sampling by searching the maximum expectation improvement point or the minimum of surrogate prediction point within the trust region. And the trust region is updated according to the current known information. The iteration continues until the potential global solution of the true optimization problem satisfied the convergence conditions. Compared with the trust region method and the biased sampling method, the proposed optimization strategy can obtain the global optimal solution to the test case, in which improvements in computation efficiency are also shown. When applied to an aerodynamic design optimization problem, the aerodynamic performance of tandem UAV is improved while meeting the constraints, which verifies its engineering application.

9.

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems 被引次数：3





胡晓敏 张军 李耘《计算机科学技术学报》,2008年第23卷第1期


Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed ＂continuous orthogonal ant colony＂ （COAC）, whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an ＂adaptive regional radius＂ method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

10.

Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming





Ming BAI Yan ZHUANG Wei WANG《控制理论与应用(英文版)》,2009年第7卷第1期


An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points （GCPs） is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparityoffset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparityoffset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual supportweight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, postprocessing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.

11.

Further Improvement on Dynamic Programming forOptimal Bit Allocation





陈毅松 汪国平 董士海《计算机科学技术学报》,2003年第18卷第1期


Dynamic programming algorithms based on Lagrange multiplier method is often used for obtaining an optimal bit allocation strategy to minimize the total distortion given a constrained rate budget in both source and channel coding applictions.Due to possible large quantizer set and improper initialization,the algorithm often suffers from heavy computational complexity.There have been may solutions in recent years to the above question.In this paper,a simple but efficient algorithm is presented to further speed up the convergence of the algorithm.This algorithm can be easily realized and get the final solution much faster.The experimental result shows that our new algorithm can figure out the optimal solution with a speed 57 times faster than the original algorithm.

12.

HPSObased fuzzy neural network control for AUV





Lei ZHANG Yongjie PANG Yumin SU Yannan LIANG《控制理论与应用(英文版)》,2008年第6卷第3期


A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSObased controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.

13.

Facial Landmark Localization by Gibbs Sampling





Bofei Wang Diankai Zhang Chi Zhang Jiani Hu Weihong Deng《中兴通讯技术（英文版）》,2014年第4期


In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP crite rion. Conventional gradient ascent algorithms get stuck at the local optimal solution. Gibbs sampling is a kind of Markov Chain Monte Carlo （MCMC） algorithm. We choose it for optimization because it is easy to implement and it guarantees global conver gence. The posterior distribution is obtained by learning prior distribution and likelihood function. Prior distribution is assumed Gaussian. We use Principle Component Analysis （PCA） to reduce the dimensionality and learn the prior distribution. Local Linear Support Vector Machine （LLSVM） is used to get the likelihood function of every key point. In our experiment, we compare our de tector with some other wellknown methods. The results show that the proposed method is very simple and efficient. It can avoid trapping in local optimal solution.

14.

基于混合微分进化算法的生化过程动态参数估计(英文)





赵超 许巧玲 林思铭 李学来《中国化学工程学报》,2013年第21卷第2期


Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with GaussNewton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of GaussNewton method to determine the optimum in the identified regions. Some wellknown benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.

15.

FPGA PLACEMENT OPTIMIZATION BY TWOSTEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM





Yang Meng A.E.A. Almaini Wang Pengjun《电子科学学刊(英文版)》,2006年第23卷第4期


Genetic Algorithm （GA） is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing （SA） converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the stateoftheart results obtained by Versatile Place and Route （VPR） Tool.

16.

Using genetic algorithm based simulated annealing penalty function to solve groundwater management model 被引次数：8





吴剑锋 朱学愚 刘建立《中国科学E辑(英文版)》,1999年第42卷第5期


The genetic algorithm (GA) is a global and random search procedure based on the mechanics of natural selection and natural genetics. A new optimization method of the genetic algorithmbased simulated annealing penalty function (GASAPF) is presented to solve groundwater management model. Compared with the traditional gradientbased algorithms, the GA is straightforward and there is no need to calculate derivatives of the objective function. The GA is able to generate both convex and nonconvex points within the feasible region. It can be sure that the GA converges to the global or at least nearglobal optimal solution to handle the constraints by simulated annealing technique. Maximum pumping example results show that the GASAPF to solve optimization model is very efficient and robust.

17.

Layered heuristic algorithm for multiple restriction routes





戴伏生《哈尔滨工业大学学报(英文版)》,2010年第17卷第1期


A layered algorithm by bidirectional searching is proposed in this paper to solve the problem that it is difficult and time consuming to reach an optimal solution of the route search with multiple parameter restrictions for good quality of service. Firstly, a set of reachable paths to each intermediate node from the source node and the sink node based on adjacent matrix transformation are calculated respectively. Then a temporal optimal path is selected by adopting the proposed heuristic method according to a nonlinear cost function. When the total number of the accumulated nodes by bidirectional searching reaches n2, the paths from two directions to an intermediate node should be combined and several paths via different nodes from the source node to the sink node can be obtained, then an optimal path in the whole set of paths can be taken as the output route. Some simulation examples are included to show the effectiveness and efficiency of the proposed method. In addition, the proposed algorithm can be implemented with parallel computation and thus, the new algorithm has better performance in time complexity than other algorithms. Mathematical analysis indicates that the maximum complexity in time, based on parallel computation, is the same as the polynomial complexity of O(kn23kn+k), and some simulation results are shown to support this analysis.

18.

A hybrid genetic algorithm based on mutative scale chaos optimization strategy 被引次数：10





YanWang HongweiSun 等《北京科技大学学报(英文版)》,2002年第9卷第6期


In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms,a new hybrid gentic algorithm is proposed.In this algorithm,a mutative scale chaos optimization strategy is operated on the population after a genetic operation.And according to the searching process.The searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population.The algorithm has such advantages as fast search,precise results and convenient using etc.The simulation results show that the performance of the method is better than that of simple genetic algorithms.

19.

Backbone analysis and algorithm design for the quadratic assignment problem 被引次数：1





He Jiang XianChao Zhang GuoLiang Chen and MingChu Li《中国科学F辑(英文版)》,2008年第51卷第5期


As the hot line in NPhard problems research in recent years, backbone analysis is crucial for phase transition, hardness, and algorithm design. Whereas theoretical analysis of backbone and its applications in algorithm design are still at a begin ning state yet, this paper took the quadratic assignment problem （QAP） as a case study and proved by theoretical analysis that it is NPhard to find the backbone, i.e., no algorithm exists to obtain the backbone of a QAP in polynomial time. Results of this paper showed that it is reasonable to acquire approximate backbone by inter section of local optimal solutions. Furthermore, with the method of constructing biased instances, this paper proposed a new metaheuristic  biased instance based approximate backbone （BIAB）, whose basic idea is as follows： firstly, construct a new biased instance for every QAP instance （the optimal solution of the new instance is also optimal for the original one）; secondly, the approximate backbone is obtained by intersection of multiple local optimal solutions computed by some existing algorithm; finally, search for the optimal solutions in the reduced space by fixing the approximate backbone. Work of the paper enhanced the research area of theoretical analysis of backbone. The metaheuristic proposed in this paper provided a new way for general algorithm design of NPhard problems as well.

20.

A genetic algorithm for the pareto optimal solution set of multiobjective shortest path problem





胡仕成 徐晓飞 战德臣《哈尔滨工业大学学报(英文版)》,2005年第12卷第6期


Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhiobjective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo rithm MSPPGA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPPGA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.
