改进鲸鱼优化算法在电力负荷调度中的应用

为了提高鲸鱼优化算法(WOA)的全局优化性能, 提出了一种基于黄金分割搜索的改进鲸鱼优化算法(GWOA)。首先利用黄金分割搜索对WOA的初始种群进行初始化, 使得初始种群能够尽可能的靠近全局最优解, 然后利用黄金分割搜索所形成的变区间, 进行变区间黄金分割非均匀变异操作, 以增加WOA的粒子多样性和提高粒子跳出局部最优陷阱的能力, 从而改善WOA的寻优性能。选取了15个大规模测试函数进行数值仿真测试, 仿真结果和统计分析表明GWOA的寻优性能要优于对比文献的改进鲸鱼优化算法(IWOA)。此外, 将GWOA用于对工程实际应用领域中的电力负荷优化调度问题进行实例分析, 实例应用结果表明, GWOA能有效对电力负荷优化调度问题进行寻优求解。     

求解带时间窗车辆路径问题的混合Memetic算法

为提高带时间窗车辆路径问题的求解精度和求解效率,设计了一种混合Memetic算法。采用基于时间窗升序排列的混合插入法构造初始种群,提高解质量的同时兼顾多样性,扩大搜索空间;任意选择组成父代种群,以维持搜索空间;运用简化的变邻域搜索进行局部开发,引入邻域半径减少策略提高开发效率,约束放松机制开放局部空间;以弧为对象,增加种群向当前最优解和全局最优解的后学习过程。实验结果表明,所提出的算法具有较好的寻优精度和稳定性,能搜索到更好的路径长度结果,更新了现有研究在最短路径长度的目标函数上的下限。     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

顺序相依拆卸线平衡多目标优化模型及算法研究

拆卸是产品回收过程最关键环节之一,拆卸效率直接影响再制造成本。本文在分析现有模型不足基础上,考虑最小化总拆卸时间,建立多目标顺序相依拆卸线平衡问题优化模型,并提出了一种自适应进化变邻域搜索算法。所提算法引入种群进化机制,并采用一种组合策略构建初始种群,通过锦标赛法选择个体进化;在局部搜索时,设计了邻域结构自适应选择策略,并采用基于交叉的全局学习机制加速跳出局部最优,以提高算法寻优能力。对比实验结果,证实了所提模型的合理性以及算法的高效性。     

基于动态引力常数和种群递减的引力搜索算法

为进一步增强标准引力搜索算法的全局搜索能力及克服其早熟收敛等缺点,提出了一种基于动态引力常数和种群递减的引力搜索算法.首先,所提算法通过动态调整引力常数,有效地平衡算法的全局搜索和局部开发能力;其次,为提高算法的性能,对种群规模进行动态递减调整,从而有效地加强了算法后期的收敛速度.最后,用30个CEC2014测试函数的仿真实验结果与3种引力搜索算法的比较说明所提算法收敛速度快,寻优能力强.     

改进的布谷鸟算法求解物流配送中心选址问题

针对基本布谷鸟算法求解物流配送中心选址问题时存在搜索精度低、易陷入局部最优值的缺陷,提出一种改进的布谷鸟算法.算法采用基于寄生巢适应度值排序的自适应方法改进基本布谷鸟算法的惯性权重,以平衡算法的全局开发能力和局部探索能力;利用NEH领域搜索以提高算法的搜索精度和收敛速度;引入停止阻止策略对全局最优寄生巢位置进行变异避免算法陷入局部最优值、增加种群的多样性.通过实验仿真表明,改进的布谷鸟算法在求解物流配送中心选址问题上要优与基本布谷鸟算法以及其它智群算法,是一种有效的算法.     

基于梯度的自适应快速布谷鸟搜索算法

针对标准布谷鸟搜索(CS)算法存在全局搜索和局部搜索能力不平衡的缺点, 提出一种基于梯度的自适应快速布谷鸟搜索(GBAQCS)算法. 在改进的算法中, 针对偏好随机游动的步长, 在利用目标函数的梯度决定步长方向的基础上, 首先提出自适应搜索机制平衡了算法的全局搜索和局部搜索能力; 其次提出快速 搜索策略, 充分利用当前鸟巢信息进行精细化搜索, 从而提高算法的搜索精度和收敛速度. 实验结果表明, 相比其他算法, 所提出的改进策略使算法的全局搜索和局部搜索能力保持了相对的平衡, 并提高了算法的收敛性能.     

改进二元分布估计算法求解置换流水车间调度问题

针对置换流水车间调度这类组合最优化问题的求解,提出了一种改进二元分布估计算法(Improved binary estimation distribution algorithm, I-EDA)。算法以二元分布估计算法为架构,使用NEH(Nawaz-Enscore-Ham)启发式算法生成初始解,提高了初始解的质量;通过对优势解的统计采样构建位置矩阵模型和链接矩阵模型,依照两个矩阵模型的合并概率组合链接区块产生子代。提出了NEH插入式重组策略和基于位置概率的交换策略和两种全新局部搜索机制替代原二元分布估计算法的相邻交换法,以进一步筛选优势解。最后通过对Reeves标准测试集的仿真实验和算法比较验证了所提出算法的有效性。     

基于改进量子遗传优化的模糊C均值聚类图像分割

针对模糊C均值聚类算法对初始聚类中心值敏感和抗噪声能力差的问题,提出一种基于改进的量子遗传优化初始聚类中心的算法,改进双链编码的量子遗传算法增加了全局搜索能力,改变传统的FCM算法计算迭代慢和易陷入局部极值的问题.同时引入空间邻域信息,利用加权隶属度矩阵建立适应度函数来改善对噪声的鲁棒性,实验结果表明,算法具有很好的分割效果和较强的抗噪能力.     

分层混合局部搜索策略异构多核系统调度

针对遗传算法解决异构多核系统的任务调度问题容易产生早熟现象及其局部寻优能力较差的缺点,将局部搜索算法与遗传算法相结合,创新性地提出一种求解异构多核系统的任务调度问题的分层混合局部搜索遗传算法。该算法提出一种新的分层优化策略以产生初始种群,在变异操作中,对部分个体设计3-opt优化变异,对种群中的优秀个体用改进的Lin-Kernighan算法进行优化。仿真实验结果表明,分层混合局部搜索遗传算法求解异构多核系统的任务调度问题时可以高效获得高质量的解。     

A Hybrid Metaheuristic for the Quadratic Assignment Problem

The quadratic assignment problem (QAP) is known to be NP-hard. We propose a hybrid metaheuristic called ANGEL to solve QAP. ANGEL combines the ant colony optimization (ACO), the genetic algorithm (GA) and a local search method (LS). There are two major phases in ANGEL, namely ACO phase and GA phase. Instead of starting from a population that consists of randomly generated chromosomes, GA has an initial population constructed by ACO in order to provide a good start. Pheromone acts as a feedback mechanism from GA phase to ACO phase. When GA phase reaches the termination criterion, control is transferred back to ACO phase. Then ACO utilizes pheromone updated by GA phase to explore solution space and produces a promising population for the next run of GA phase. The local search method is applied to improve the solutions obtained by ACO and GA. We also propose a new concept called the eugenic strategy intended to guide the genetic algorithm to evolve toward a better direction. We report the results of a comprehensive testing of ANGEL in solving QAP. Over a hundred instances of QAP benchmarks were tested and the results show that ANGEL is able to obtain the optimal solution with a high success rate of 90%. This work was supported in part by the National Science Council, R.O.C., under Contract NSC 91-2213-E-005-017.     

Ant colonies for the quadratic assignment problem

This paper presents HAS–QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS–QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS–QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS–QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS–QAP performance is less competitive on random, regular and unstructured problems.     

COSEARCH: A Parallel Cooperative Metaheuristic

In order to design a well-balanced metaheuristic for robustness, we propose the COSEARCH approach which manages the cooperation of complementary heuristic methods via an adaptive memory which contains a history of the search already done. In this paper, we present the idiosyncrasies of the COSEARCH approach and its application for solving large scale instances of the quadratic assignment problem (QAP). We propose an original design of the adaptive memory in order to focus on high quality regions of the search and avoid attractive but deceptive areas. For the QAP, we have hybridized three heuristic agents of complementary behaviours: a Tabu Search is used as the main search algorithm, a Genetic Algorithm is in charge of the diversification and a Kick Operator is applied to intensify the search. The evaluations have been executed on large scale network of workstations via a parallel environment which supports fault tolerance and adaptive dynamic scheduling of tasks.     

Nonlinear optimisation using directional step lengths based on RPROP

A search method based on the backpropagation rule commonly used for training neural networks is proposed here for the optimisation of smooth nonlinear functions. The use of the Resilient backPROPagation (RPROP) heuristic rule for local minimisation is described. The details of employing the directional step length determined by RPROP along with a simple restarting scheme are provided. In the approach proposed here direct use of the directional step determined by the heuristic without using any line search conditions takes place. The overall algorithm has been tested on a number of benchmark functions found in the literature with very positive results. The test problems’ dimension ranges from 100 to 50,000. The results obtained show that the suggested search direction method results to a highly efficient algorithm suitable for large scale optimisation.     

Iterated local search for the quadratic assignment problem

Iterated local search (ILS) is a simple and powerful stochastic local search method. This article presents and analyzes the application of ILS to the quadratic assignment problem (QAP). We justify the potential usefulness of an ILS approach to this problem by an analysis of the QAP search space. However, an analysis of the run-time behavior of a basic ILS algorithm reveals a stagnation behavior which strongly compromises its performance. To avoid this stagnation behavior, we enhance the ILS algorithm using acceptance criteria that allow moves to worse local optima and we propose population-based ILS extensions. An experimental evaluation of the enhanced ILS algorithms shows their excellent performance when compared to other state-of-the-art algorithms for the QAP.     

求解旅行商问题的基于类Kruskal的混合粒子群算法

本文针对求解旅行商问题的标准粒子群算法所存在的早熟和低效的问题,提出一种基于Greedy Heuristic的初始解与粒子群相结合的混合粒子群算法(SKHPSO)。该算法通过本文给出的类Kruskal算法作为Greedy Heuristic的具体实现手段,产生一个较优的初始可行解,作为粒子群中的一员,然后再用改进的混合粒子群算法进行启发式搜索。SKHPSO的局部搜索借鉴了Lin-Kernighan邻域搜索,而全局搜索结合了遗传算法中的交叉及置换操作。应用该算法对TSPLIB中的典型算例进行了算法测试分析,结果表明:SKHPSO可明显提高求解的质量和效率。     

Continuous functions minimization by dynamic random search technique

Random search technique is the simplest one of the heuristic algorithms. It is stated in the literature that the probability of finding global minimum is equal to 1 by using the basic random search technique, but it takes too much time to reach the global minimum. Improving the basic random search technique may decrease the solution time. In this study, in order to obtain the global minimum fastly, a new random search algorithm is suggested. This algorithm is called as the Dynamic Random Search Technique (DRASET). DRASET consists of two phases, which are general search and local search based on general solution. Knowledge related to the best solution found in the process of general search is kept and then that knowledge is used as initial value of local search. DRASET’s performance was experimented with 15 test problems and satisfactory results were obtained.     

Minimizing the total completion time in a two-machine flowshop with sequence-independent setup times

We consider the problem of minimizing the sum of completion times in a two-machine permutation flowshop subject to setup times. We propose a new priority rule, several constructive heuristics, local search procedures, as well as an effective multiple crossover genetic algorithm. Computational experiments carried out on a large set of randomly generated instances provide evidence that a constructive heuristic based on newly derived priority rule dominates all the proposed constructive heuristics. More specifically, we show that one of our proposed constructive heuristics outperforms the best constructive heuristic in the literature in terms of both error and computational time. Furthermore, we show that one of our proposed local search-based heuristics outperforms the best local search heuristic in the literature in terms of again both error and computational time. We also show that, in terms of quality-to-CPU time ratio, the multiple crossover genetic algorithm performs consistently well.     

Generating quadratic assignment test problems with known optimal permutations

The quadratic assignment problem (QAP) is a well-known combinatorial optimization problem of which the travelling-salesman problem is a special case. Although the QAP has been extensively studied during the past three decades, this problem remains very hard to solve. Problems of sizes greater than 15 are generally impractical to solve. For this reason, many heuristics have been developed. However, in the literature, there is a lack of test problems with known optimal solutions for evaluating heuristic algorithms. Only recently Paulubetskis proposed a method to generate test problems with known optimal solutions for a special type of QAP. This paper concerns the generation of test problems for the QAP with known optimal permutations. We generalize the result of Palubetskis and provide test-problem generators for more general types of QAPs. The test-problem generators proposed are easy to implement and were also tested on several well-known heuristic algorithms for the QAP. Computatinal results indicate that the test problems generated can be used to test the effectiveness of heuristic algorithms for the QAP. Comparison with Palubetskis' procedure was made, showing the superiority of the new test-problem generators. Three illustrative test problems of different types are also provided in an appendix, together with the optimal permutations and the optimal objective function values.     

A GRASP for the biquadratic assignment problem

The biquadratic assignment problem (BiQAP) is a generalization of the quadratic assignment problem (QAP). It is a nonlinear integer programming problem where the objective function is a fourth degree multivariable polynomial and the feasible domain is the assignment polytope. BiQAP problems appear in VLSI synthesis. Due to the difficulty of this problem, only heuristic solution approaches have been proposed. In this paper, we propose a new heuristic for the BiQAP, a greedy randomized adaptive search procedure (GRASP). Computational results on instances described in the literature indicate that this procedure consistently finds better solutions than previously described algorithms.