一种用于片上网络布图规划的改进模拟退火与粒子群混合算法

智能优化算法作为解决大规模集成电路芯片设计中布图规划问题的经典方法已被研究多年。结合异构三维片上网络布图问题的具体特点,采用B*-tree间接描述布图问题中的解结构,针对模拟退火收敛速度慢、优化效率低的缺点,对搜索策略和概率性的劣向转移作出了改进,并将改进后的模拟退火思想引入粒子群优化算法中,使结合后的算法结合了粒子群并行计算的特点和模拟退火能够实现全局优化的特点。通过仿真实验验证,所提出的该混合改进算法在解决布图问题中要优于传统模拟退火算法。     

Capacitated Clustering Problems by Hybrid Simulated Annealing and Tabu Search

The capacitated clustering problem (CCP) is the problem in which a given set of weighted objects is to be partitioned into clusters so that the total weight of objects in each cluster is less than a given value (cluster ‘capacity’). The objective is to minimize the total scatter of objects from the ‘centre’ of the cluster to which they have been allocated. A simple constructive heuristic, a R-interchange generation mechanism, a hybrid simulated annealing (SA) and tabu search (TS) algorithm which has computationally desirable features using a new non-monotonic cooling schedule, are developed. A classification of the existing SA cooling schedules is presented. The effects on the final solution quality of the initial solutions, the cooling schedule parameters and the neighbourhood search strategies are investigated. Computational results on randomly generated problems with size ranging from 50 to 100 customers indicate that the hybrid SA/TS algorithm out-performs previous simulated annealing algorithms, a simple tabu search and local descent algorithms.     

Occupancy estimation from environmental parameters using wrapper and hybrid feature selection

Occupancy information is essential to facilitate demand-driven operations of air-conditioning and mechanical ventilation (ACMV) systems. Environmental sensors are increasingly being explored as cost effective and non-intrusive means to obtain the occupancy information. This requires the extraction and selection of useful features from the sensor data. In past works, feature selection has generally been implemented using filter-based approaches. In this work, we introduce the use of wrapper and hybrid feature selection for better occupancy estimation. To achieve a fast computation time, we introduce a ranking-based incremental search in our algorithms, which is more efficient than the exhaustive search used in past works. For wrapper feature selection, we propose the WRANK-ELM, which searches an ordered list of features using the extreme learning machine (ELM) classifier. For hybrid feature selection, we propose the RIG-ELM, which is a filter–wrapper hybrid that uses the relative information gain (RIG) criterion for feature ranking and the ELM for the incremental search. We present experimental results in an office space with a multi-sensory network to validate the proposed algorithms.     

New Genetic Algorithms Based Approaches to Continuous <Emphasis Type="Italic">p</Emphasis>-Median Problem

We proposed new genetic algorithms (GAs) to address well-known p-median problem in continuous space. Two GA approaches with different replacement procedures are developed to solve this problem. To make the approaches more efficient in finding near-optimal solution two hybrid algorithms are developed combining the new GAs and a traditional local search heuristic. The performance of the newly developed models is compared to that of the traditional alternating location-allocation heuristics by numerical simulation and it is found that the models are effective in finding optimum facility locations.     

Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The proposed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrapper model included, to large and high-dimensional domains. The experiments performed show that our two strategies are competitive and can select a small subset of features without degrading the classification error or the advantages of the strategies under study.     

Mathematical modeling and heuristic approaches to flexible job shop scheduling problems

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problem with traditional optimization approaches owing to the high computational complexity. For solving the realistic case with more than two jobs, two types of approaches have been used: hierarchical approaches and integrated approaches. In hierarchical approaches assignment of operations to machines and the sequencing of operations on the resources or machines are treated separately, i.e., assignment and sequencing are considered independently, where in integrated approaches, assignment and sequencing are not differentiated. In this paper, a mathematical model and heuristic approaches for flexible job shop scheduling problems (FJSP) are considered. Mathematical model is used to achieve optimal solution for small size problems. Since FJSP is NP-hard problem, two heuristics approaches involve of integrated and hierarchical approaches are developed to solve the real size problems. Six different hybrid searching structures depending on used searching approach and heuristics are presented in this paper. Numerical experiments are used to evaluate the performance of the developed algorithms. It is concluded that, the hierarchical algorithms have better performance than integrated algorithms and the algorithm which use tabu search and simulated annealing heuristics for assignment and sequencing problems consecutively is more suitable than the other algorithms. Also the numerical experiments validate the quality of the proposed algorithms.     

Optimal switch location in mobile communication networks using hybrid genetic algorithms

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.     

A memetic algorithm for evolutionary prototype selection: A scaling up approach

Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. Evolutionary algorithms have been used recently for prototype selection showing good results. However, due to the complexity of this problem when the size of the databases increases, the behaviour of evolutionary algorithms could deteriorate considerably because of a lack of convergence. This additional problem is known as the scaling up problem.

Memetic algorithms are approaches for heuristic searches in optimization problems that combine a population-based algorithm with a local search. In this paper, we propose a model of memetic algorithm that incorporates an ad hoc local search specifically designed for optimizing the properties of prototype selection problem with the aim of tackling the scaling up problem. In order to check its performance, we have carried out an empirical study including a comparison between our proposal and previous evolutionary and non-evolutionary approaches studied in the literature.

The results have been contrasted with the use of non-parametric statistical procedures and show that our approach outperforms previously studied methods, especially when the database scales up.     

A Neurogenetic approach for the resource-constrained project scheduling problem

A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.     

Utilizing the advantages of both global and local search strategies for finding a small subset of features in a two-stage method

Feature selection (FS) is one of the pre-processing methods that are widely used in the fields of Data Mining and Pattern Recognition. Elimination of redundant/irrelevant features of large data sets and finding a suitable feature subset are one of the main goals in FS. The utilization of evolutionary algorithms, including global search algorithms e.g. Genetic Algorithm and local search algorithms e.g. hill climbing, is known as the best way to solve a variety of optimization problems such as FS problem. They are never able to find a globally optimal solution because they are often trapped in one of the local optimum solutions and stop. Therefore, the researchers have tried to solve this major problem by escaping from the local solutions. In this article, we propose a two-stage method by applying a global search algorithm and a local search algorithm to find a sub-optimal solution for the FS problem. Here, we define a sub-optimal solution as a solution with the high reduction rate and the similar or even better classification performance. In the suggested two-stage method referred to as BGSA-SA, that is, the binary version of the Gravitational Search Algorithm (BGSA) and Simulated Annealing (SA) are selected as global and local search algorithms, respectively. For evaluating this proposed two-stage method, we utilized several UCI machine learning datasets and both classifiers SVM and K-NN. We compare the accuracy and reduction rate of the proposed two-stage method with three groups of methods, such as: (1) six singular meta-heuristic methods including BGA, BPSO, GSAPSO, CHGSA, BGSA, and SA, (2) the other two-stage methods namely BGA-SA and BPSO-SA, and (3) seven published methods as the state-of-art methods. The obtained results confirm that our BGSA-SA method has the rank 1 in the reduction rate whereas the accuracy of it using both SVM and K-NN classifiers is similar or even, in some cases, better than the other mentioned methods.     

Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems

Classification is one of the important tasks in data mining. The probabilistic neural network (PNN) is a well-known and efficient approach for classification. The objective of the work presented in this paper is to build on this approach to develop an effective method for classification problems that can find high-quality solutions (with respect to classification accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridizes the firefly algorithm with simulated annealing (denoted as SFA), where simulated annealing is applied to control the randomness step inside the firefly algorithm while optimizing the weights of the standard PNN model. We also extend our work by investigating the effectiveness of using Lévy flight within the firefly algorithm (denoted as LFA) to better explore the search space and by integrating SFA with Lévy flight (denoted as LSFA) in order to improve the performance of the PNN. The algorithms were tested on 11 standard benchmark datasets. Experimental results indicate that the LSFA shows better performance than the SFA and LFA. Moreover, when compared with other algorithms in the literature, the LSFA is able to obtain better results in terms of classification accuracy.     

Hybrid deep learning model for answering visual medical questions

Due to the increase in electronic documents containing medical information, the search for specific information is often complex and time-consuming. This has prompted the development of new tools designed to address this issue. Automated visual question/answer (VQA) systems are becoming more challenging to develop. These are computer programs that take images and questions as input and then combine all inputs to generate text-based answers. Due to the enormous amount of question and the limited number of specialists, many issues stay unanswered. It’s possible to solve this problem by using automatic question classifiers that guide queries to experts based on their subject preferences. For these purposes, we propose a VQA approach based on a hybrid deep learning model. The model consists of three steps: (1) the classification of medical questions based on a BERT model; (2) image and text feature extraction using a deep learning model, more specifically the extraction of medical image features by a hybrid deep learning model; and (3) text feature extraction using a Bi-LSTM model. Finally, to predict the appropriate answer, our approach uses a KNN model. Additionally, this study examines the influence of the Adam, AdaGrad, Stochastic gradient descent and RMS Prop optimization techniques on the performance of the network. As a consequence of the studies, it was shown that Adam and SGD optimization algorithms consistently produced higher outcomes. Experiments using the ImageCLEF 2019 dataset revealed that the suggested method increases BLEU and WBSS values considerably.

     

求解多维0—1背包问题的混合遗传算法

文章研究一类典型的组合优化问题——多维０－１背包问题,提出了在简单遗传算法（ＳＧＡ）中加入局部搜索机制的混合遗传算法（ＨＧＡ）来求解该类问题,并在大量数值实验的基础上,将ＨＧＡ与传统的求解方法及ＳＧＡ进行了比较,实验的结果表明,该算法具有一定的优越性。     

A Memetic Approach to the Nurse Rostering Problem

Constructing timetables of work for personnel in healthcare institutions is known to be a highly constrained and difficult problem to solve. In this paper, we discuss a commercial system, together with the model it uses, for this rostering problem. We show that tabu search heuristics can be made effective, particularly for obtaining reasonably good solutions quickly for smaller rostering problems. We discuss the robustness issues, which arise in practice, for tabu search heuristics. This paper introduces a range of new memetic approaches for the problem, which use a steepest descent improvement heuristic within a genetic algorithm framework. We provide empirical evidence to demonstrate the best features of a memetic algorithm for the rostering problem, particularly the nature of an effective recombination operator, and show that these memetic approaches can handle initialisation parameters and a range of instances more robustly than tabu search algorithms, at the expense of longer solution times. Having presented tabu search and memetic approaches (both with benefits and drawbacks) we finally present an algorithm that is a hybrid of both approaches. This technique produces better solutions than either of the earlier approaches and it is relatively unaffected by initialisation and parameter changes, combining some of the best features of each approach to create a hybrid which is greater than the sum of its component algorithms.     

Bi-objective optimisation model for installation scheduling in offshore wind farms

A bi-objective optimisation using a compromise programming approach is proposed for installation scheduling of an offshore wind farm. As the installation cost and the completion period of the installation are important aspects in the construction of an offshore wind farm, the proposed method is used to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal installation schedule considering several constraints such as weather condition and the availability of vessels. We suggest two approaches to deal with the multi-objective installation scheduling problem, namely compromise programming with exact method and with metaheuristic techniques. In the exact method the problem is solved by CPLEX whereas in the metaheuristic approach we propose Variable Neighbourhood Search (VNS) and Simulated Annealing (SA). Moreover, greedy algorithms and a local search for solving the scheduling problem are introduced. Two generated datasets are used for testing our approaches. The computational experiments show that the proposed metaheuristic approaches produce interesting results as the optimal solution for some cases is obtained.     

A comparative analysis of meta-heuristic approaches for facility layout design problem: a case study for an elevator manufacturer

In this study, a facility layout problem having NP-hard problem characteristic is attempted to be solved by using two different meta-heuristic approaches—Genetic Algorithm (GA) and Simulated Annealing (SA)—and a hybrid approach—Genetic Algorithm/Simulated Annealing (HGASA). The case study is completed for a company which can be seen as a small or a medium size enterprise. First, parameter values of GA and SA are determined by testing for various combinations of them. Then, the algorithms are run for one hundred times. The results of the algorithms are compared based on their fitness values and calculation time requirements using the paired-t test, mean and standard values. The results show that SA performs better than the others in terms of the fitness values and the time requirements. In this study, we also test the performance of our GA, SA and HGASA methodologies using some of the well-known test problems from the literature. We obtain very close results to those in literature.     

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

In the present study, strength of meta-heuristic computing techniques is exploited for estimation problem of Hammerstein controlled auto regressive auto regressive moving average (HCARARMA) system using differential evolution (DE), genetic algorithms (GAs), pattern search (PS) and simulated annealing (SA) algorithms. The approximation theory in mean squared error sense is utilized for construction of cost function for HCARARMA model and highly uncorrelated adjustable parameter of the system is optimized with global search exploration of DE, GAs, PS and SA algorithms. Comparative study is carried out from desired known parameters of the HCARARMA model for different degree of freedom and noise variation scenarios. Performance analysis of the DE, GAs, PS and SA algorithms is conducted through results of statistics based on sufficient large independent executions in terms of measure of central tendency and variation for both precision and complexity indices. The exhaustive simulations established that the population-based heuristics are more accurate than single solution-based methodologies for HCARARMA identification.     

产生式与判别式线性混合分类器

产生式方法和判别式方法是解决分类问题的两种不同框架,具有各自的优势。为利用两种方法各自的优势,文中提出一种产生式与判别式线性混合分类模型,并设计一种基于遗传算法的产生式与判别式线性混合分类模型的学习算法。该算法将线性混合分类器混合参数的学习看作一个最优化问题,以两个基分类器对每个训练数据的后验概率值为数据依据,用遗传算法找出线性混合分类器混合参数的最优值。实验结果表明,在大多数数据集上,产生式与判别式线性混合分类器的分类准确率优于或近似于它的两个基分类器中的优者。     

Hybridization of fuzzy GBML approaches for pattern classification problems

We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we examine the search ability of each approach to efficiently find fuzzy rule-based systems with high classification accuracy. It is clearly demonstrated that each approach has its own advantages and disadvantages. Next, we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is handled as an individual. Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set. Then, we compare our hybrid algorithm with the Michigan and Pittsburgh approaches. Experimental results show that our hybrid algorithm has higher search ability. The necessity of a heuristic specification method of antecedent fuzzy sets is also demonstrated by computational experiments on high-dimensional problems. Finally, we examine the generalization ability of fuzzy rule-based classification systems designed by our hybrid algorithm.