Multi-objective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations

This study addresses the resource-constrained project scheduling problem with precedence relations, and aims at minimizing two criteria: the makespan and the total weighted start time of the activities. To solve the problem, five multi-objective metaheuristic algorithms are analyzed, based on Multi-objective GRASP (MOG), Multi-objective Variable Neighborhood Search (MOVNS) and Pareto Iterated Local Search (PILS) methods. The proposed algorithms use strategies based on the concept of Pareto Dominance to search for solutions and determine the set of non-dominated solutions. The solutions obtained by the algorithms, from a set of instances adapted from the literature, are compared using four multi-objective performance measures: distance metrics, hypervolume indicator, epsilon metric and error ratio. The computational tests have indicated an algorithm based on MOVNS as the most efficient one, compared to the distance metrics; also, a combined feature of MOG and MOVNS appears to be superior compared to the hypervolume and epsilon metrics and one based on PILS compared to the error ratio. Statistical experiments have shown a significant difference between some proposed algorithms compared to the distance metrics, epsilon metric and error ratio. However, significant difference between the proposed algorithms with respect to hypervolume indicator was not observed.     

Assembly sequence planning of rigid and flexible parts

Assembly sequence planning (ASP) is the process of computing a sequence of assembly motions for constituent parts of an assembled final product. ASP is proven to be NP-hard and thus its effective and efficient solution has been a challenge for the researchers in the field. Despite the fact that most assembled products like ships, aircrafts and automobiles are composed of rigid and flexible parts, no work exists for assembly/disassembly sequence planning of flexible parts. This paper lays out a theoretical ground for modeling the deformability of flexible assembly parts by introducing the concept of Assembly stress matrix (ASM) to describe interference relations between parts of an assembly and the amount of compressive stress needed for assembling flexible parts. Also, the Scatter Search (SS) optimization algorithm is customized for this problem to produce high-quality solutions by simultaneously minimizing both the maximum applied stress exerted for performing assembly operations and the number of assembly direction changes. The parameters of this algorithm are tuned by a TOPSIS-Taguchi based tuning method. A number of ASP problems with rigid and flexible parts were solved by the presented SS and other algorithms like Genetic and Memetic algorithms, Simulated Annealing, Breakout Local Search, Iterated Local Search, and Multistart Local Search, and the results and their in-depth statistical analyses showed that the SS outperformed other algorithms by producing the best-known or optimal solutions with highest success rates.     

A hybrid clonal selection algorithm for the location routing problem with stochastic demands

In this paper, a new formulation of the Location Routing Problem with Stochastic Demands is presented. The problem is treated as a two phase problem where in the first phase it is determined which depots will be opened and which customers will be assigned to them while in the second phase, for each of the open depots a Vehicle Routing Problem with Stochastic Demands is solved. For the solution of the problem a Hybrid Clonal Selection Algorithm is applied, where, in the two basic phases of the Clonal Selection Algorithm, a Variable Neighborhood Search algorithm and an Iterated Local Search algorithm respectively have been utilized. As there are no benchmark instances in the literature for this form of the problem, a number of new test instances have been created based on instances of the Capacitated Location Routing Problem. The algorithm is compared with both other variants of the Clonal Selection Algorithm and other evolutionary algorithms.     

Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Evolutionary multi-objective optimization algorithms are generally employed to generate Pareto optimal solutions by exploring the search space. To enhance the performance, exploration by global search can be complemented with exploitation by combining it with local search. In this paper, we address the issues in integrating local search with global search such as: how to select individuals for local search; how deep the local search is performed; how to combine multiple objectives into single objective for local search. We introduce a Preferential Local Search mechanism to fine tune the global optimal solutions further and an adaptive weight mechanism for combining multi-objectives together. These ideas have been integrated into NSGA-II to arrive at a new memetic algorithm for solving multi-objective optimization problems. The proposed algorithm has been applied on a set of constrained and unconstrained multi-objective benchmark test suite. The performance was analyzed by computing different metrics such as Generational distance, Spread, Max spread, and HyperVolume Ratio for the test suite functions. Statistical test applied on the results obtained suggests that the proposed algorithm outperforms the state-of-art multi-objective algorithms like NSGA-II and SPEA2. To study the performance of our algorithm on a real-world application, Economic Emission Load Dispatch was also taken up for validation. The performance was studied with the help of measures such as Hypervolume and Set Coverage Metrics. Experimental results substantiate that our algorithm has the capability to solve real-world problems like Economic Emission Load Dispatch and is able to produce better solutions, when compared with NSGA-II, SPEA2, and traditional memetic algorithms with fixed local search steps.     

LDE融合光谱回归分类的光照变化人脸识别

针对光照变化人脸识别问题中传统的光谱回归算法不能很好地进行特征提取而严重影响识别性能的问题,提出了局部判别嵌入优化光谱回归分类的人脸识别算法。计算出训练样本的特征向量;借助于数据的近邻和分类关系,利用局部判别嵌入算法构建分类问题所需的嵌入,同时学习每种分类的子流形所需的嵌入;利用光谱回归分类算法计算投影矩阵,并利用最近邻分类器完成人脸的识别。在两大人脸数据库扩展YaleB及CMU PIE上的实验验证了该算法的有效性,实验结果表明,相比其他光谱回归算法,该算法取得了更高的识别率、更好的工作特性,并且降低了计算复杂度。     

通用扫描线多边形填充算法

传统的扫描线多边形填充算法只适用于水平扫描线的逐行填充。文章提出通用扫描线多边形填充算法,该算法可以有效地解决任意间距、任意倾角的扫描线对多边形的填充问题。通用扫描线多边形算法采用了坐标变换、浮点数舍入策略等重要方法。顶点扫描线号是该算法中的核心概念。     

Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang–Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks − five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.     

The max-min hill-climbing Bayesian network structure learning algorithm

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html. Editor: Andrew W. Moore     

Multiple Phase Neighborhood Search-GRASP for the Capacitated Vehicle Routing Problem

Greedy Randomized Adaptive Search Procedure (GRASP) has been proved to be a very efficient algorithm for the solution of the Traveling Salesman Problem. Also, it has been proved that expanding the local search with the use of two or more different local search strategies helps the algorithm to avoid trapping in a local optimum. In this paper, a new modified version of GRASP, called Multiple Phase Neighborhood Search-GRASP (MPNS-GRASP), for the solution of the Vehicle Routing Problem is proposed. In this method, a stopping criterion based on Lagrangean Relaxation and Subgradient Optimization is utilized. In addition, a different way for expanding the neighborhood search is used based on a new strategy, the Circle Restricted Local Search Moves strategy. The algorithm was tested on two sets of benchmark instances and gave very satisfactory results. In both sets of instances the results have solution qualities with average values near to the optimum values and in a number of them the algorithm finds the optimum. The computational time of the algorithm is decreased significantly compared to other heuristic and metaheuristic algorithms due to the fact that the new strategy, the Expanding Neighborhood Search Strategy, is used.     

基因-表现型的布谷鸟算法求解旅行商问题

布谷鸟搜索（Cuckoo Search,CS）算法在求解连续优化问题时表现出了较好的性能,但现有的CS算法在求解旅行商问题（Traveling Salesman Problem,TSP）时收敛较慢且未能体现Levy飞行的特点,针对这些不足提出了一种新的基因-表现型的布谷鸟算法（Genotype-Phenotype Cuckoo Search,GPCS）,GPCS算法首先赋予每个城市一个整数部分为城市编号的随机小数编码即基因,而此基因所表现的内容由小数和整数共同决定,小数决定城市的访问次序,整数部分代表某个城市,两个部分组合起来构成Levy飞行的邻域空间,最后根据不同的飞行结果选择重定位或替换操作。实验结果表明,GPCS算法优于同类的CS算法,也优于一些其他的群智能算法,特别在求解大规模TSP时其优势更加明显。     

移动通讯系统中固定网络的优化设计

搜索技术，诸如遗传算法，模拟退火算法，禁忌搜索和随机移动算法，已经广泛应用于全局优化。本文重点介绍了采用这些搜索算法优化设计蜂窝网络固定网部分性能比较的实验分析。在讨论不同算法的特性参数对算法性能的影响基础上，在相同的约束条件下使用MatLab对这些算法进行了比较实验，结果表明：在给定的问题模式和网络节点位置的假定下，禁忌搜索算法和遗传算法能提供较好的、稳定的解决方案。     

Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.     

On parallel scan-conversion algorithms for transputer networks

Two fundamental scan line procedures are considered from the point of view of parallel implementation on transputer networks. The paper describes algorithms for the scan conversion of polygons, and the related hidden surface elimination problem for three-dimensional models with polygonal data structures. In each case parallel designs have been implemented and timed in various configurations against a functionally equivalent, but not necessarily optimized, single processor code. The results presented demonstrate that scan line algorithms can be efficiently implemented on suitably configured networks of transputers.     

Benchmarking Explicit State Parallel Model Checkers

This paper presents a set of benchmarks and metrics for performance reporting in explicit state parallel model checking algorithms. The benchmarks are selected for controllability, and the metrics are chosen to measure speedup and communication overhead. The benchmarks and metrics are used to compare two parallel model checking algorithms: partition and random walk. Implementations of the partition algorithm using synchronous and asynchronous communication are used. Metrics are reported for each benchmark and algorithm for up to 128 workstations using a network of dynamically loaded workstations. Empirical results show that load balancing becomes an issue for more than 32 workstations in the partition algorithm and that random walk is a reasonable, low overhead, approach for finding errors in large models. The synchronous implementation is consistently faster than the asynchronous. The benchmarks, metrics and results given here are intended to be a starting point for a larger discussion of performance reporting in parallel explicit state model checking.     

Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy.     

基于Z-buffer算法优化的大型变电站场景模型快速线消隐方法

针对现有三维模型消隐方法面向大规模三维场景模型应用中存在的计算复杂、耗时长等缺陷, 本文提出了基于改进 Z-buffer 算法对大型变电站场景消隐的快速可视化方法。首先,为了简化计算,将场景模 型数据整合并重构;其次,通过透视投影变换将变电场景模型像素化;进一步,基于 Z-buffer 算法高效的像素 化计算特性提出了快速模型筛选方法,从而得到变电场景的子模型遮挡关系。最后,实验中将所得遮挡关系列 表融合现有消隐算法,结果表明本文提出的方法能够大幅度提升消隐的运算性能。     

一种适用于H．264的基于自适应搜索范围的快速运动估计算法

块匹配运动估计在视频编码中有着举足轻重的地位，其性能的优劣在很大程度上影响着输出码流的质量。全搜索是效果最好的运动估计算法，但其巨大的运算量是实际应用，特别是实时应用无法承受的。为解决这一问题，各种快速算法不断涌现。本文提出了一种适用于最新视频编码标准——H．264／MPEG4-AVC的快速运动估计算法。该算法基于自适应搜索范围，利用视频图像序列的帧间统计特性以及运动向量时域、空域的相关性，在保证PSNR性能的同时，使运动估计部分的运算复杂度大为降低。实验仿真表明，该算法适用面广，对大运动与小运动序列都有很强的自适应能力。在保持与全搜索相同PSNR的同时，平均速度超过全搜索280倍有余，超过三步法1．8倍，性能明显优于新三步法、四步法等经典快速运动估计算法。     

Automated network management and configuration using Probabilistic Trans-Algorithmic Search

Online configuration of large-scale systems such as networks requires parameter optimization within a limited amount of time, especially when configuration is needed as a response to recover from a failure in the system. To quickly configure such systems in an online manner, we propose a Probabilistic Trans-Algorithmic Search (PTAS) framework which leverages multiple optimization search algorithms in an iterative manner. PTAS applies a search algorithm to determine how to best distribute available experiment budget among multiple optimization search algorithms. It allocates an experiment budget to each available search algorithm and observes its performance on the system-at-hand. PTAS then probabilistically reallocates the experiment budget for the next round proportional to each algorithm’s performance relative to the rest of the algorithms. This “roulette wheel” approach probabilistically favors the more successful algorithm in the next round. Following each round, the PTAS framework “transfers” the best result(s) among the individual algorithms, making our framework a trans-algorithmic one. PTAS thus aims to systematize how to “search for the best search” and hybridize a set of search algorithms to attain a better search. We use three individual search algorithms, i.e., Recursive Random Search (RRS) (Ye and Kalyanaraman, 2004), Simulated Annealing (SA) (Laarhoven and Aarts, 1987), and Genetic Algorithm (GA) (Goldberg, 1989), and compare PTAS against the performance of RRS, GA, and SA. We show the performance of PTAS on well-known benchmark objective functions including scenarios where the objective function changes in the middle of the optimization process. To illustrate applicability of our framework to automated network management, we apply PTAS on the problem of optimizing link weights of an intra-domain routing protocol on three different topologies obtained from the Rocketfuel dataset. We also apply PTAS on the problem of optimizing aggregate throughput of a wireless ad hoc network by tuning datarates of traffic sources. Our experiments show that PTAS successfully picks the best performing algorithm, RRS or GA, and allocates the time wisely. Further, our results show that PTAS’ performance is not transient and steadily improves as more time is available for search.     

一个有效的多边形窗口的线裁剪算法

已有的线剪裁算法都是针对矩形窗口或凸多边形窗口的,对于一的多边形窗口（包括凹多边形）的线剪裁,目前尚无有效的算法,而这样的算法却有更普遍的应用意义。该文提出一个对于一般多边形窗口的线剪裁算法。该算法在被裁剪直线的延长线上取一固定点,然后求多边形窗口的每一顶点到该固定点引线的斜率。这样对于每个窗口边只需判断被裁剪直线的斜率是否在该边两顶点到固定点引线斜率之间,就可判定直线与边是否相交,因此,每处理一