求解k条最短路径问题的混合蛙跳算法

提出了一种求解k条最短路径问题的混合蛙跳算法.采用自然路径的形式对青蛙个体编码,设计了一种能够使模因信息在青蛙个体间传递的蛙跳方法.在各青蛙族群内部,通过较差个体向优秀个体的跳跃进行局部搜索,从而优化模因信息.在族群之间,通过混合与排序使各族群的模因信息得以交流与重组,从而获取新的寻优方向.数值实验表明,本文算法搜索k条最短路径的能力强、收敛速度快、稳定性好,可应用于求解大规模网络中的多条最优路径问题.     

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection.     

Hybridization of evolutionary algorithms and local search by means of a clustering method.

This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are usually called hybrid algorithms. On the other hand, it is well known that the clustering process enables the creation of groups (clusters) with mutually close points that hopefully correspond to relevant regions of attraction. Local-search procedures can then be started once in every such region. This paper proposes the combination of an EA, a clustering process, and a local-search procedure to the evolutionary design of product-units neural networks. In the methodology presented, only a few individuals are subject to local optimization. Moreover, the local optimization algorithm is only applied at specific stages of the evolutionary process. Our results show a favorable performance when the regression method proposed is compared to other standard methods.     

A multiobjective hybrid genetic algorithm for the capacitatedmultipoint network design problem

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.     

Flow-shop sequencing using hybrid simulated annealing

Simulated annealing (SA) heuristics have been successfully applied on a variety of complex optimization problems. This paper presents a new hybrid SA approach for the permutation flow-shop scheduling (FSS) problem. FSS is known to be NP-hard, and thus the right way to proceed is through the use of heuristics techniques. The proposed approach combines the characteristics of a canonical SA procedure together with features borrowed from the field of genetic algorithms (GAs), such as the use of a population of individuals and the use of a novel, non-standard recombination operator for generating solutions. The approach is easily implemented and performs near-optimal schedules in a rather short computation time. Experiments over multiple benchmarks test problems show that the developed approach has higher performance than that of other FSS meta-heuristic approaches, generating schedules of shorter makespans faster. The experiments include comparisons between the proposed hybrid model, a genetic algorithm, and two other standard simulated annealing approaches. The final solutions obtained by the method are within less than 1% in average from the optimal solutions obtained so far.     

A hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines

This paper presents a hybrid memetic algorithm for the problem of scheduling n jobs on m unrelated parallel machines with the objective of maximizing the weighted number of jobs that are completed exactly at their due dates. For each job, due date, weight, and the processing times on different machines are given. It has been shown that when the numbers of machines are a part of input, this problem is NP-hard in the strong sense. At first, the problem is formulated as an integer linear programming model. This model is practical to solve small-size problems. Afterward, a hybrid memetic algorithm is implemented which uses two heuristic algorithms as constructive algorithms, making initial population set. A data oriented mutation operator is implemented so as to facilitate memetic algorithm search process. Performance of all algorithms including heuristics (H1 and H2), hybrid genetic algorithm and hybrid memetic algorithm are evaluated through computational experiments which showed the capabilities of the proposed hybrid algorithm.     

基于自适应混合启发算法求解一类JSP问题

为解决一类Job Shop问题，提出了一种具有自适应机制的新的混合算法。该算法在分析和比较模拟退火算法和遗传算法的基础上，针对它们都缺乏全局指导机制的共同问题，引入具有自适应能力的全局指导策略，建立起个体与种群之间的反馈机制，混合后的算法还综合了两种启发算法的各自优点。通过具体的算例验证了该算法的有效性。     

A novel hybrid DE–random search approach for unit commitment problem

Differential evolution (DE) is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it very attractive for numerical optimization. DE uses a rather greedy and less stochastic approach to problem-solving than other evolutionary algorithms. DE combines simple arithmetic operators with the classical operators of recombination, mutation and selection to evolve from a randomly generated starting population to a final solution. Although global exploration ability of DE algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low and it suffers from the problem of untime convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of DE algorithm is presented in the proposed research. This research paper presents a hybrid version of DE algorithm combined with random search for the solution of single-area unit commitment problem. The hybrid DE–random search algorithm is tested with IEEE benchmark systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by experimental analysis, it has been found that proposed algorithm yields global results for the solution of unit commitment problem.

     

机器人逆运动学差分自适应混沌粒子群求解

采用D-H法通过连杆坐标系变换矩阵建立机械臂运动控制模型,该模型呈现非常严重的非线性特性,传统方法难以求解。由于动态差分算法具有很强的全局搜索能力,而粒子群算法具有精确的局部搜索能力的特点,融合改进的动态差分算法和粒子群算法,并引入混沌映射初始种群和粒子群学习因子与惯性权重的自适应算法,提出多子群分层差分自适应混沌粒子群算法。该算法采用的多子群分层结构能提升个体共享群体信息的能力,底层利用动态差分算法进行全局搜索,顶层精英群利用改进的粒子群算法进行局部搜索。仿真试验和实际应用表明该算法在稳定性、搜索成功率以及收敛精度有显著提高,能有效解决机器人逆运动学模型的求解。     

Path relinking for the vertex separator problem

This paper presents the first population-based path relinking algorithm for solving the NP-hard vertex separator problem in graphs. The proposed algorithm employs a dedicated relinking procedure to generate intermediate solutions between an initiating solution and a guiding solution taken from a reference set of elite solutions (population) and uses a fast tabu search procedure to improve some selected intermediate solutions. Special care is taken to ensure the diversity of the reference set. Dedicated data structures based on bucket sorting are employed to ensure a high computational efficiency. The proposed algorithm is assessed on four sets of 365 benchmark instances with up to 20,000 vertices, and shows highly comparative results compared to the state-of-the-art methods in the literature. Specifically, we report improved best solutions (new upper bounds) for 67 instances which can serve as reference values for assessment of other algorithms for the problem.     

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.     

基于文化算法的家庭服务机器人任务规划

通过对家庭服务机器人任务规划问题进行形式化描述,给出了问题的求解模型,提出了一种改进的文化算法,通过算法中信念空间和种群空间的相互联系和相互促进实现求解。算法采用独特的编码方式,其种群空间采用遗传算法作为进化手段,采用较为独特的信念提取方式构造算法的信念空间并促使其进化。将该算法用于家庭服务机器人大赛的仿真平台上,证明其有效性。     

A Genetic Algorithm Based on Relaxation Induced Neighborhood Search in a Local Branching Framework for Capacitated Multicommodity Network Design

The fixed-charge Capacitated Multicommodity Network Design (CMND) is a well-known problem of both practical and theoretical significance. This article proposes the Genetic Algorithm (GA) cooperative Relaxation Induced Neighborhood Search (RINS) in a Local Branching (LB) framework for CMND problem. GA algorithm is started by initial population which is made by two parents obtain from hybrid LB and RINS algorithms. The basic idea of the proposed solution method is to use the GA algorithm to explore the search space and the hybrid LB and RINS methods to move from current solution to neighbor solution. Adapting the metaheuristic algorithm with RINS method to fit within an LB framework represents an interesting challenge. To evaluate the proposed algorithm, the standard problems with different sizes are used. The parameters of the algorithm are tuned by design of experiments. In order to prove the efficiency and effectiveness of the proposed algorithm, the results are compared with the best results available in the literature. The statistical analysis shows high performance of the proposed algorithm.     

差分准对立学习多目标蚁狮算法

为解决多目标优化问题，对经典的蚁狮算法进行改进，提出了基于差分进化的准对立学习多目标蚁狮算法（DEQOMALO）。该算法针对蚁狮算法易陷入局部最优的不足，一方面，该算法引用差分进化的思想，充分利用种群和精英蚁狮的信息对原算法中蚂蚁个体的位置更新方式进行改进；另一方面采用反向学习策略对蚂蚁种群进行优化，将原种群个体和其准对立个体进行混合并择优作为新的种群，大大增加种群的多样性。选取典型的标准测试函数，将提出的算法与原始蚁狮算法以及其他传统进化策略优化的蚁狮算法进行比较。实验结果表明，改进算法在收敛性和分布性上均有很大程度的提升，在解决双目标优化问题上具有较好的鲁棒性和有效性。     

Annealing-based heuristics and genetic algorithms for circuit partitioning in parallel test generation

In this paper simulated annealing and genetic algorithms are applied to the graph partitioning problem. These techniques mimic processes in statistical mechanics and biology, respectively, and are the most popular meta-heuristics or general-purpose optimization strategies. A hybrid algorithm for circuit partitioning, which uses tabu search to improve the simulated annealing meta-heuristics, is also proposed and compared with pure tabu search and simulated annealing algorithms, and also with a genetic algorithm. The solutions obtained are compared and evaluated by including the hybrid partitioning algorithm in a parallel test generator which is used to determine the test patterns for the circuits of the frequently used ISCAS benchmark set.     

A new hybrid feature selection approach using feature association map for supervised and unsupervised classification

Feature selection, both for supervised as well as for unsupervised classification is a relevant problem pursued by researchers for decades. There are multiple benchmark algorithms based on filter, wrapper and hybrid methods. These algorithms adopt different techniques which vary from traditional search-based techniques to more advanced nature inspired algorithm based techniques. In this paper, a hybrid feature selection algorithm using graph-based technique has been proposed. The proposed algorithm has used the concept of Feature Association Map (FAM) as an underlying foundation. It has used graph-theoretic principles of minimal vertex cover and maximal independent set to derive feature subset. This algorithm applies to both supervised and unsupervised classification. The performance of the proposed algorithm has been compared with several benchmark supervised and unsupervised feature selection algorithms and found to be better than them. Also, the proposed algorithm is less computationally expensive and hence has taken less execution time for the publicly available datasets used in the experiments, which include high-dimensional datasets.     

基于改进蛙跳算法的分布式两阶段混合流水车间调度

针对考虑顺序相关准备时间的分布式两阶段混合流水车间调度问题,提出一种改进的蛙跳算法以同时最小化拖后工件数和最大完成时间.该算法通过启发式方法和随机方法对种群进行初始化,采取基于种群和记忆的种群划分方法,同时给出模因组质量评价方法,并根据模因组质量将所有模因组划分为最优模因组、最差模因组和其他模因组,每种类型的模因组分别采取不同的搜索策略,并分配不同的搜索次数,其中最优模因组不参与种群划分.选用一种多目标经典算法和两种近5年提出的算法作为对比算法,并与改进蛙跳算法的变体进行比较以验证模因组搜索新策略的有效性.通过对大量实例的计算实验结果表明,模因组搜索新策略有效,改进蛙跳算法能有效求解分布式两阶段混合流水车间调度问题.     

基于动态拥挤距离的混合多目标免疫优化算法

多目标免疫优化算法的研究目标是种群均匀分布于优化问题的非劣最优域并使算法快速收敛。为进一步提高多目标优化问题非支配解集合的分布均匀性和收敛性,提出了一种基于动态拥挤距离的混合多目标免疫优化算法。该算法基于动态拥挤距离来对个体进行比较和更新操作,从而保持最终解集的均匀分布,同时借鉴经典差分进化算法中的变异引导算子来加强免疫优化算法的局部搜索能力并提高搜索精度。基于5个经典测试函数的仿真结果表明, 与其他几种有效的多目标优化算法相比,所提算法不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且收敛速度也有较大的改进和提高。     

An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers

In this paper, an effective hybrid discrete differential evolution (HDDE) algorithm is proposed to minimize the maximum completion time (makespan) for a flow shop scheduling problem with intermediate buffers located between two consecutive machines. Different from traditional differential evolution algorithms, the proposed HDDE algorithm adopted job permutation to represent individuals and applies job-permutation-based mutation and crossover operations to generate new candidate solutions. Moreover, a one-to-one selection scheme with probabilistic jumping is used to determine whether the candidates will become members of the target population in next generation. In addition, an efficient local search algorithm based on both insert and swap neighborhood structures is presented and embedded in the HDDE algorithm to enhance the algorithm’s local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It shows that the proposed HDDE algorithm is not only capable to generate better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization algorithm, but outperforms two recently proposed discrete differential evolution (DDE) algorithms as well. Especially, the HDDE algorithm is able to achieve excellent results for large-scale problems with up to 500 jobs and 20 machines.     

Multiple-model based predictive control of nonlinear hybrid systems based on global optimization using the Bernstein polynomial approach

In this paper, we propose a Bernstein polynomial based global optimization algorithm for the optimal feedback control of nonlinear hybrid systems using a multiple-model approach. Specifically, we solve at every sampling instant a polynomial mixed-integer nonlinear programming problem arising in the model predictive control strategy. The proposed algorithm uses the Bernstein polynomial form in a branch-and-bound framework, with new ingredients such as branching for integer decision variables and fathoming for each subproblem in the branch-and-bound tree. The performance of the proposed algorithm is tested and compared with existing algorithms on a benchmark three-spherical tank system. The test results show the superior performance of the proposed algorithm.