IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA) . This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, differential evolution algorithm, and Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with Nash–Sutcliffe Efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

We propose a highly efficient searching algorithm in crystal structure prediction. The searching algorithm is a hybrid of the evolutionary algorithm and Bayesian optimization. The evolutionary algorithm is widely used in crystal structure prediction, and the Bayesian optimization is one of the selection-type algorithms we have developed. We have performed simulations of crystal structure prediction to compare the success rates of the random search, evolutionary algorithm, Bayesian optimization, and hybrid algorithm for up to ternary systems such as Si, Y2Co17, Al2O3, and CuGaS2, using the CrySPY code. These results demonstrate that the evolutionary algorithm can generate structures more efficiently than random structure generation, and the Bayesian optimization can efficiently select potential candidates from a large number of structures. Moreover, the hybrid algorithm, which has the advantages of both, is proved to be the most efficient searching algorithm among them.

The maximum cut (MAX-CUT) problem is to find a bipartition of the vertices in a given graph such that the number of edges with ends in different sets reaches the largest. Though, several experimental investigations have shown that evolutionary algorithms (EAs) are efficient for this NP-complete problem, there is little theoretical work about EAs on the problem. In this paper, we theoretically investigate the performance of EAs on the MAX-CUT problem. We find that both the (1+1) EA and the (1+1) EA*, two simple EAs, efficiently achieve approximation solutions of (m/2)+(1/4)s(G) and (m/2)+(1/2)(√{8m+1}-1), where m and s(G) are respectively the number of edges and the number of odd degree vertices in the input graph. We also reveal that for a given integer k the (1+1) EA* finds a cut of size at least k in expected runtime O(nm+1/δ(4k)) and a cut of size at least (m/2)+k in expected runtime O(n(2)m+1/δ((64/3)k(2))), where δ is a constant mutation probability and n is the number of vertices in the input graph. Finally, we show that the (1+1) EA and the (1+1) EA* are better than some local search algorithms in one instance, and we also show that these two simple EAs may not be efficient in another instance.

• A new dynamic benchmark suite with configurable predictability degree of the optima. • Severity, direction and randomness of the optima’s movement, can be chosen. • Introduction of a new performance metric for prediction-driven optimisers. • Seven state-of-the-art evolutionary algorithms for dynamic optimisation are assessed. Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requirement satisfaction problems such as Robust Optimization Over Time. The benchmark problems in these studies inherently use randomly changing parameters and therefore such randomness may make it difficult to compare these algorithms with other EDO approaches. In this paper, we introduce a new benchmark, called Moving Peaks Benchmark with Attractors, which incorporates an attractor heuristic that attracts peaks to a certain location in the environment into the moving peaks problem. The proposed benchmark is fully flexible where the dynamics of the attractors and the rate at which a peak is attracted to such attractors can be modified. By adjusting these characteristics, certain styles of movements can be achieved by a peak. We also introduce a new performance measure that focuses on the comparison of algorithms that use prediction. Seven EDO algorithms based on different working logics are chosen to give a wide representation of the state-of-the-art in this area. We argue that having predictable characteristics in the benchmark problem is more adequate for studying the performances and behaviours of those algorithms that embed prediction mechanisms. Experimental results obtained with the proposed benchmark show it’s suitability for the EDO domain as all algorithms featuring prediction capabilities display higher accuracy than their competitors.

The performance monitoring of computer systems is a complex affair, made even more challenging by the increasing gap between hardware and software. Methods that collect and feed data to performance analysis can usually be classified into one of two groups. Methods in the first group (e.g., software instrumentation), can be accurate but have unacceptable overheads and offer no insight into the interaction of the software and the hardware. Methods in the second group (e.g., hardware supported) run in real time and connect code to the response in the architecture, at a considerable decrease in accuracy, with a high degree of specificity to the hardware and therefore with limited actionable information made available. In this work, we focus on the latter group - the collection and analysis of raw performance data sourced from hardware Performance Monitoring Units, built into the majority of processors. The periodic collection of samples (Event Based Sampling), containing code locations, for instance, can be triggered on events such as retired instructions or cache misses. We quantify accuracy improvements to existing methods, propose a better performing method of performance data collection and a new architecture-agnostic method of performance data analysis. In our experiments, we analyze compiled, large-scale production workloads, industry standard benchmarks, and micro-benchmarks reproducing specific architectural behaviors. First, we focus on performance data collection methods and their accuracy, when establishing instruction retirement rates. These vary from least to most advanced and we examine the low level factors influencing accuracy. In particular, we employ a method based on Last Branch Records, a hardware facility present on some Intel architectures, which allows for the sampling of short records of last taken branches leading up to the sample. We compare the most commonly used method, its improved derivatives and the Last Branch Record based method. With respect to the first method, we observe accuracy improvements of up to 18x on synthetic benchmarks and 15x on real applications. Second, to further improve accuracy, we propose a new collection method named Hybrid Basic Block Profiling, fusing those based on Last Branch Records and the widely used Event Based Sampling. We apply simple machine learning techniques to determine the operating criteria. As a demonstration of capability, our method and a profiling tool are used to generate Instruction Mixes, which traditionally require good quality data at the most detailed level of granularity - basic blocks. Compared to software instrumentation, we observe an improvement in runtime of up to 76x, while keeping instruction attribution errors at 2.1% on average. In our third contribution we describe an architecture-agnostic performance analysis methodology called Hierarchical Cycle Accounting. The user is presented with a navigable hierarchy of microarchitectural issues, expressed in a metric that is universal and simple to compare - core cycles. We develop a reference implementation for the Intel Ivy Bridge microarchitecture and provide pointers for implementations on other processor architectures. Our approach and tool are successfully used by non-experts to improve and vectorize complex scientific code. Experts benefit as well - by being able to pinpoint software issues undetected by other methods, and identifying a bug in a family of Intel processors.

In recent years, computer network communication technology has made considerable progress and development, and its scale has now covered all aspects of society and people’s lives. However, while computer network technology promotes social development and improves the convenience of people’s lives, there are still many security problems, especially the difficulty of ensuring the security of user data, which has become a prominent problem in the further development and improvement of communication technology. This article takes the network communication security technology as an entry point, and analyzes its technical types with the aid of data encryption technology, and then analyzes and studies its security in computer network communication data transmission according to the specific points proposed. Through the substantial technical analysis of the computer network security system, the possibility of the system being attacked can be reduced to a certain extent, and the security of network data can be improved. Through the analysis of the integrity of the network system, the overall framework of a distributed network security early warning system based on the network is established, and various structures of the system are analyzed. The network load analysis module is responsible for using key performance index data, such as user traffic, number of users and wireless utilization, and analyzes and classifies the network cell load by combining the partial least square method and BP neural network, so that the key cell performance analysis module is mainly Analyze key performance index data of key communities, obtain early warning thresholds through clustering algorithms, and implement a dynamic early warning system to provide guidance for network planning and mining in key small areas. The database module is responsible for storing all performance data and performance analysis results; the performance query module is responsible for querying and analyzing performance data and static warnings, and presents them to users in the form of text and graphics. Experimental research results show that in this information age, the development of computer networks plays a very important role in the development of our country. Computer network operators make full use of cluster analysis, data mining, encryption algorithms, and early warning systems to conduct research to fully improve the security performance of data in network communications.

Performance analysis is an important step in tuning performance critical applications. It is a cyclic process of measuring and analyzing performance data which is driven by the programmers hypotheses on potential performance problems. Currently this process is controlled manually by the programmer. The implicit knowledge applied in this cyclic process must be formalized in order to be reused in the automation of performance analysis tools. This article describes the performance property specification language ASL developed in the APART Esprit IV working group. ASL allows the specification of performance data via an object model and of performance properties via a specially designed notation. Performance bottlenecks can then be identified based on the specification since bottlenecks are viewed as performance properties with a huge negative impact. We present the ASL language in the context of MPI applications.

To address the increasing complexity in parallel and distributed systems and software, advances in performance technology towards more robust tools and broader, more portable implementations are needed. In doing so, new challenges for performance instrumentation, measurement, analysis, and visualization arise to address evolving requirements for how performance phenomena is observed and how performance data is used. This paper presents recent advances in the TAU performance system in four areas where improvements in performance technology are important: instrumentation control, performance mapping, performance interaction and steering, and performance databases. In the area of instrumentation control, we are concerned with the removal of instrumentation in cases of high measurement overhead. Our approach applies rule-based analysis of performance data in an iterative instrumentation process. Work on performance mapping focuses on measuring performance with respect to dynamic calling paths when the static callgraph cannot be determined prior to execution. We describe an online performance data access, analysis, and visualization system that will form the basis of a large-scale performance interaction and steering system. Finally, we describe our approach to the management of performance data in a database framework that supports multi-experiment analysis.

The evolutions computation is the best proceeding algorithm for all kinds of optimization problem in the world. Bayesian optimization algorithm (BOA) is one kind of the evolution algorithm which is advantage on others for high order, hierarchical and correlative on anther optimization problem. For improving the ability of the BOA, the decision graph was introduced to enhance the represent and learn of Bayesian network and compress the parameter saving. The optimization mechanism and the algorithm model were studied in detail. The evaluation indexes and test function were also built for validating the merits. The performance and efficiency of DBOA were analyzed with compare with BOA, basic genetic algorithm and binary particle swarm optimization. The test results showed that DBOA was effective for hierarchical decomposable function.

PurposeThe purpose of this research is to examine an empirical model of marketing strategy and shareholder value through customer satisfaction and financial performance by using a value‐based marketing approach on organisations listed in Amman Stock Exchange Market operating in Jordan.Design/methodology/approachA quantitative methodology was employed through conducting two surveys targeting Jordanian organisations' marketing managers and their customers as well as utilising hard measures for financial performance and shareholder value data analysis. A total of 218 marketing managers were involved in the managers' survey and 1,200 customers were involved in the customers' satisfaction survey. A series of exploratory and confirmatory factor analyses were used to assess the research constructs dimensions, unidimensionality, validity and composite reliability. Structural path model analysis was also used to test the hypothesised research model.FindingsThe empirical results indicate that external and internal marketing orientations (IMOs) exerted a positive and significant effect on marketing strategy components, namely: product, price, promotion and distribution strategies. Marketing strategy components exerted a positive and significant effect on customer satisfaction. The findings also indicate that product and price strategies are the strongest drivers of customer satisfaction. Customer satisfaction has a positive and significant effect on financial performance. More importantly, financial performance has a strong positive contribution to shareholder value measured by market value added and earning per share.Originality/valueThis is the first empirical research paper that has investigated a model of marketing strategy and shareholder value through customer satisfaction and financial performance especially in developing countries, e.g. Jordan. This research offered executives and marketing directors empirical evidence on drivers of shareholder value maximisation and how to enhance marketing's strategic influence on strategic decisions which were not available to them before.

Many optimization problems from the industrial engineering world, in particular the manufacturing systems, are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization tech niques called evolutionary algorithms, which can often outperform conventional optimization methods when applied to difficult real-world problems. There are currently three main avenues of this research: genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs). Among them, genetic algorithms are perhaps the most widely known types of evolutionary algorithms today. During the past years, several GAs for the job-shop scheduling problems have been proposed, each with different chromosome representation. In this paper, the different GAs are collected from the literature and an attempt has been made to evaluate them. The benchmark problems available in open literature are used for evaluation and the performance measure considered is makespan. The algorithms are coded in C+ +.

This research explores how child protection is constructed through performance measurement, and how the selection and reporting of performance indicators influences policy and practice. Performance measurement is now a mainstay of public sector management. At one level, it has a role in addressing ever-present questions about the quality and effectiveness of child protection services. However, there are concerns about assessing performance within a management framework, rather than a professional or client-centred framework. Therefore, the thesis aims to critically engage with the problems and potential of performance measurement in child protection with a view to maximising its capacity to promote better outcomes for children and families. It focuses particularly on the role of family support, because despite its proclaimed importance in legislation and policy, in practice family support work remains marginalised, situated 'outside' core child protection work. The research approaches performance measurement from a critical policy perspective, in which indicators have both technical and policy functions. While performance measurement may be viewed rationally as a means for government to manage resources, from a broader policy perspective the way it is conceptualised and implemented has major consequences for service delivery. The policy significance of performance measurement is explored through two studies. The first study analyses developments in policy and practice in three jurisdictions: England, Victoria (Australia) and Queensland (Australia). This analysis contrasts the prominent position of family support in child protection legislation and policy, with its marginal position in both practice and performance measurement. It outlines the socio-political context for performance measurement in child protection, drawing out the values and power relations embedded in performance indicators that give them a role in communicating policy intent. The study concludes that performance measurement in all three jurisdictions is directing policy attention (and therefore resources) towards investigation and out-of-home care, largely ignoring family support. The second study moves beyond the policy critique to more concrete concerns about how to reconstruct performance measurement in child protection to encompass family support indicators. Using a survey method, it seeks to describe and analyse the practice of family support in one jurisdiction (Queensland) according to a performance measurement model of inputs, outputs, processes and outcomes. It concludes that useful performance data can be collected to aid understanding of the contribution of family support to the overall child protection system. Such data will help to move family support beyond assumptions about its value, towards empirical scrutiny of its methods and results. It suggests that family support will command greater policy attention if included in the child protection performance measurement effort. The thesis concludes that viewed from a critical policy perspective, performance measurement can be harnessed in the interests of good practice. It can assist with the repositioning of family support, moving it from the realm of unproblematic moral goodness to more critical inquiry. It argues there is nothing inherently wrong with measuring effectiveness and efficiency, but it is important to examine the manner in which performance measurement is developed and implemented, its methods and consequences. Performance indicators are not neutral or merely technical. Establishing indicators can influence how social problems such as child abuse and neglect are defined, where resources are placed, what service types are funded, and how outcomes for children and families are defined. It questions the privilege performance indicators may be given within managerial approaches to government, as if they somehow provide an objective and unbiased account of how services are performing. However, this is not to suggest that performance indicators are worthless or meaninglessmrather that their meaning should be explicit, and their underpinning value stances debated within policy processes. Such debate will enhance the congruency between child protection research and indicators, and democratise the processes by which performance measures are developed, used and reported.

The continuous p-median problem (CPMP) is one of the most popular and widely used models in location theory that minimizes the sum of distances from known demand points to the sought points called centers or medians. This NP-hard location problem is also useful for clustering (automatic grouping). In this case, sought points are considered as cluster centers. Unlike similar k-means model, p-median clustering is less sensitive to noisy data and appearance of the outliers (separately located demand points that do not belong to any cluster). Local search algorithms including Variable Neighborhood Search as well as evolutionary algorithms demonstrate rather precise results. Various algorithms based on the use of greedy agglomerative procedures are capable of obtaining very accurate results that are difficult to improve on with other methods. The computational complexity of such procedures limits their use for large problems, although computations on massively parallel systems significantly expand their capabilities. In addition, the efficiency of agglomerative procedures is highly dependent on the setting of their parameters. For the majority of practically important p-median problems, one can choose a very efficient algorithm based on the agglomerative procedures. However, the parameters of such algorithms, which ensure their high efficiency, are difficult to predict. We introduce the concept of the AGGLr neighborhood based on the application of the agglomerative procedure, and investigate the search efficiency in such a neighborhood depending on its parameter r. Using the similarities between local search algorithms and (1 + 1)-evolutionary algorithms, as well as the ability of the latter to adapt their search parameters, we propose a new algorithm based on a greedy agglomerative procedure with the automatically tuned parameter r. Our new algorithm does not require preliminary tuning of the parameter r of the agglomerative procedure, adjusting this parameter online, thus representing a more versatile computational tool. The advantages of the new algorithm are shown experimentally on problems with a data volume of up to 2,000,000 demand points.

Classification is important task of data mining used to extract knowledge from huge volume of data. By nature, classification is multi-objective problem, as it required optimization of multiple objectives simultaneously like accuracy, sensitivity, squared error, precision etc. Traditionally, evolutionary algorithms were used to solve multi-objective classification problem by considering it as single-objective problem, but this approach gives single solution to problem. Therefore, multi-objective evolutionary algorithms are used to solve classification problem. In this paper, we have used Pareto approach to optimize neural network to solve classification problem. Non-dominated sorting genetic algorithm is used to simultaneously optimize accuracy and mean squared error objectives of neural network along with local search. As slow convergence to optimal solutions is major disadvantage of evolutionary algorithm. To speed up convergence to optimal solutions hybrid technique is adopted by augmenting evolutionary technique with local search algorithm. This proposed approach gives set of Pareto optimal solutions which represent different solutions for given classification problem.

There is increasing evidence that females are outperforming males in secondary education across a range of subjects. The data in higher education, however, is not so clear-cut. Several studies have been undertaken examining the impact of gender on undergraduate accounting performance, ranging from early year performance to that of later years, with conflicting results. Some of the literature suggests that gender differences are dependent on the type of assessment utilized, reporting that females tend to perform better than males in coursework assessments with the position being reversed for examinations. This paper examines gender differences across several performance measures both prior to and post entry into an Accounting and Finance degree. Data was collected from the population of honours graduates of 1998, 1999 and 2000 (n = 132) on a thick sandwich Accounting and Finance degree at the Robert Gordon University in Scotland. All the data was extracted from student files to develop a multitude of independent performance related variables, categorized into: point of university entry data, early university performance data and final honours year performance data (examined on an individual module basis and also a global coursework and examination performance basis). Statistical differences were examined using a two independent sample technique, whereby the population was categorized by gender into male and female, with either a t-test or Mann–Whitney test being utilized dependent on the distribution of the independent variable. Only two gender differences were found: females outperformed their male counterparts in the first year accounting module and also in the auditing module, which was undertaken via distance learning during the third year of the programme. No gender differences were found in any of the final year modules, and this was also evident in the coursework and examination performance analysis. Single sex focus groups were set up to explore why gender differences were apparent in the auditing module.