混合搜索法在显微镜自动对焦中的应用

针对基于显微镜的自动对焦系统,本文提出了一种爬山搜索法和函数逼近法相结合的混合搜索算法。该算法中的爬山搜索法采用粗精结合的两段式算法。在粗略对焦时,大步距选用速度较快的灰度方差函数;当精细对焦时,小步距采用灵敏度较高的Laplacian函数;通过比较三幅图片来缩小对焦区间并且在该区间内采用函数逼近法来拟合出最佳对焦位置。该方法不仅大大减少了自动对焦所需要的图片数量,而且可以大幅度提高搜索精度。经实验验证,提出的新的搜索算法可以使搜索精度优于1?m。

利用混合搜索法优化选煤分选指标

通过选煤厂复杂的非线性系统七种优化方法的比较 ,证明混合搜索法可以防止局部最优解 ,减少优化时间 ,保证优化结果的可靠性。

梯级电站长期负荷分配模型及其求解方法

针对以往梯级水库长期负荷分配研究只考虑空间组合而较少考虑时间组合的缺点,建立了考虑空间组合和时间组合的梯级电站长期负荷分配模型及其求解方法,并以乌江梯级水电站为实例进行验证。研究结果表明,采用混合搜索法求解梯级水库长期负荷分配问题,与快速分配法求解结果相比,调度期内可减少弃水电能1.14亿kW,并将其转化为调度期末梯级蓄能0.92亿KW,不仅确保了目标函数最优,而且计算结果能真实反映梯级水电系统实际运行情况。

基于MATLAB的同步带传动优化设计

阐述同步带传动优化设计的技术和方法,以设计轴间距与预定轴间距之间偏差最小为目标,建立数学模型;用牛顿迭代法求解反渐开线函数;用混合离散变量直接搜索法求出最优解;用MATLAB实现计算机辅助同步带传动优化设计。

Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems

A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.

Bi-level hybrid local search approach for three-dimensional loading problem with balancing constraints

This paper presents a bi-level hybrid local search(BHLS)algorithm for the three-dimensional loading problem with balancing constraints(3DLP-B),where several rectangular boxes with even densities but different sizes are loaded into a single cubic bin to meet the requirements of the space or capacity utilization and the balance of the center of gravity.The proposed algorithm hybridizes a novel framed-layout procedure in which the concept of the core block and its generation strategy are introduced.Once the block-loading sequence has been determined,we can load one block at a time by the designed construction heuristic.Then,the double-search is introduced;its external search procedure generates a list of compact packing patterns while its internal search procedure is used to search the core-block frames and their best distribution locations.The approach is extensively tested on weakly to strongly heterogeneous benchmark data.The results show that it has better performance in improving space utilization rate and balanced condition of the placement than existed techniques:the overall averages from 79.85%to 86.45%were obtained for the balanced cases and relatively high space-usage rate of 89.44%was achieved for the unbalanced ones.

A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization

Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems.This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method.Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.

A Location-Based Content Search Approach in Hybrid Delay Tolerant Networks

In Delay Tolerant Networks （DTNs）, the offiine users can, through the encountering nodes, use the specific peer-to-peer message routing approach to deliver messages to the destination. Thus, it solves the problem that users have the demands to deliver messages while they are temporarily not able to connect to the Internet. Therefore, by the characteristics of DTNs, people who are not online can still query some location based information, with the help of users using the same service in the nearby area. In this paper, we proposed a location-based content search approach. Based on the concept of three-tier area and hybrid node types, we presented four strategies to solve the query problem, namely, Data Replication, Query Replication, Data Reply, and Data Synchronization strategies. Especially we proposed a Message Queue Selection algorithm for message transferring. The priority concept is set associated with every message such that the most ＂important＂ one could be sent first. In this way, it can increase the query success ratio and reduce the query delay time. Finally, we evaluated our approach, and compared with other routing schemes. The simulation results showed that our proposed approach had better query efficiency and shorter delay.

General Pattern and Neighborhood Search Algorithms for Non-Convex Unit Commitment

This paper presents a new approach to solve mixed-variable unit commitment(UC) problems with non-smooth cost functions based on a generalized pattern search filter(GPS-filter) algorithm. A GPS-filter algorithm does not require any information about the gradient of the objective function while searching for an optimum solution. At the same time, it is available for solving mixed-variable optimization problems, which is very suitable for UC. A new suitable discrete neighborhood structure with UC characteristics is proposed to improve GPS-filter efficiently. A lot of multiple units' states are fixed before search; hence, the polling search of discrete variable is efficient for a few uncertain units. Numerical experiments are included to demonstrate the proposed approach's ability to handle the highly nonlinear, discontinuous, non-smooth cost functions and mixed variables of the UC problem.

A HYBRID PSO-SA OPTIMIZING APPROACH FOR SVM MODELS IN CLASSIFICATION

Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.