Search-based Optimization Techniques Research Articles

Development of improved harmonic compensation technique for PV-wind hybrid distributed generator connected to microgrid

PurposeThis paper provides a framework for a PV-DFIG-based microgrid-connected system to eliminate lower-order harmonics and optimise higher-order harmonics. Also, at low wind speeds and wider voltage variations, it supplies voltage to cater to the demand of the grid. Design/methodology/approachA unique series compensation strategy has been implemented to mitigate stator harmonics produced by switching at the rotor side, dependent on rotor speed. The SHEPWM technique has been employed for various switching strategies to control a wider range of harmonics injected with a minimum number of switching pulses, used online using the piecewise mixed model. This is achieved using just three triggering angles, which gives equivalently good results as seven switches, hence with reduced switching losses and higher efficiency. So, the SHEPWM technique has been utilised to control these harmonics using the least number of switches per cycle. FindingsVarious search-based optimization techniques have been compared to determine the best performance for the problem statement, and the minimum possible THD was found to be 1.8% using the recent Search and Rescue (SAR) based optimization technique, which is within the suggested international standards such as IEEE 1547, IEC, and CIGRE WG 36-05. A comparative analysis has been carried out with different literature and it was found that the SAR-based SHEPWM gives better results when undergoing proposed series compensation in order to achieve the objective of the present paper. Originality/valueA laboratory prototype has also been made to investigate the simulations whose results were found to be satisfactory.

“Sampling” as a Baseline Optimizer for Search-Based Software Engineering

Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method “Sway”, short for “the sampling way”. This paper compares Sway versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. For these models, the experiments of this paper show that Sway is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations. Considering the simplicity and effectiveness of Sway, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.

Harmony search based remodularization for object-oriented software systems

Software remodularization is always a key task in the field of software reengineering. In recent years, search-based optimization techniques have been considered as an effective method to handle software remodularization problems. Recently, Harmony Search (HS), a metaheuristic algorithm has gained wide attention and has been demonstrated to be effective and convenient to solve various science and engineering problems. The applicability and usefulness of HS algorithm has not been studied by any researcher till date to solve the software remodularization problem. This paper proposes a Harmony Search-Based Remodularization Algorithm (HSBRA) to solve the software remodularization problem for object-oriented software (OOS) systems. To do so, several key improvements have been put forward like an efficient encoding of harmony memory, initialization of harmony memory, an effective strategy for improvisation of a new harmony. In addition, a new fitness function that considers coupling, cohesion, package count index and package size index is developed. Four different variants of HSBRA (i.e., HSBRA1, HSBRA2, HSBRA3, and HSBRA4) based on linear and exponential changes in Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR) have been formulated. The proposed approach is tested over 8 problem instances and results are compared with both the population based (Genetic Algorithm - GA, Differential Evolution – DE, and Artificial Bee Colony - ABC) and single-solution based (Simulated Annealing - SA and Hill-Climbing - HC) algorithms. A Wilcoxon test is performed to assess the pair wise statistical performance of the algorithms. The results show that HSBRA outperforms SA, HC, and GA algorithms and performs better than ABC algorithms. Out of four variants of HSBRA, exponential change based variants of HSBRA perform better than linear change based variants.

FlagRemover

Search-Based Testing is a widely studied technique for automatically generating test inputs, with the aim of reducing the cost of software engineering activities that rely upon testing. However, search-based approaches degenerate to random testing in the presence of flag variables, because flags create spikes and plateaux in the fitness landscape. Both these features are known to denote hard optimization problems for all search-based optimization techniques. Several authors have studied flag removal transformations and fitness function refinements to address the issue of flags, but the problem of loop-assigned flags remains unsolved. This article introduces a testability transformation along with a tool that transforms programs with loop-assigned flags into flag-free equivalents, so that existing search-based test data generation approaches can successfully be applied. The article presents the results of an empirical study that demonstrates the effectiveness and efficiency of the testability transformation on programs including those made up of open source and industrial production code, as well as test data generation problems specifically created to denote hard optimization problems.

Utility driven optimization of real time data broadcast schedules

AbstractData dissemination in wireless environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many environments where the utility of the received data is an equally important criterion as its timeliness. Missing the deadline may reduce the utility of the data but does not necessarily make it zero. In this work, we address the problem of scheduling real time data broadcasts with such soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within an acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby eliminating the requirement of a heuristic in the process. An evolution strategy based stochastic hill-climber is investigated in this context.

Editorial for the special issue on search‐based software engineering

Editorial for the special issue on search‐based software engineering

Introduction to the special issue on search based software engineering

This special issue of Empirical Software Engineering consists of revised and extended versions of five selected papers originally presented at the 1st International Symposium on Search Based Software Engineering (SSBSE 2009). The symposium was held at Cumberland Lodge, Windsor, UK, in May 2009, and brought together the academic researchers and industrial practitioners that make up the international Search Based Software Engineering (SBSE) community for the first conference dedicated to the subject. Search Based Software Engineering is the application of search-based optimization techniques—for example, genetic algorithms, hill climbing, simulated annealing, taboo search, and ant colony optimization—to the solution of software engineering problems. Many such problems are often difficult or impossible to solve manually, and moreover, as modern software becomes more complex and adopts distributed and highly parallel architectures, are proving to be increasingly intractable using traditional automated techniques. In contrast, SBSE approaches can leverage sophisticated optimisation algorithms running on affordable computing infrastructure to solve these problems. SBSE approaches also demonstrate other advantages: they can be used to find acceptably good solutions when time, or resources, are limited, and—by utilizing multi-objective search algorithms—can derive a range of solutions that illustrate potential trade-offs between competing goals. For these reasons, both academic and industrial interest in SBSE is increasing rapidly. A recent survey,1 undertaken by the UK-based SEBASE project, indicated an exponential growth of

A Transmission Planning Framework Considering Future Generation Expansions in Electricity Markets

This paper proposes a transmission planning framework in a market environment in which only the generation sector is deregulated. The proposed framework is based on modeling generation companies' (GenCos') strategic behavior and anticipating their expansion patterns from the viewpoint of a transmission system planner. The transmission expansion planning problem in this paper is modeled as a four-level optimization problem. A solution method based on agent-based systems and search-based optimization techniques is proposed to determine the optimal transmission expansion plan.

A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search

Search-based optimization techniques have been applied to structural software test data generation since 1992, with a recent upsurge in interest and activity within this area. However, despite the large number of recent studies on the applicability of different search-based optimization approaches, there has been very little theoretical analysis of the types of testing problem for which these techniques are well suited. There are also few empirical studies that present results for larger programs. This paper presents a theoretical exploration of the most widely studied approach, the global search technique embodied by Genetic Algorithms. It also presents results from a large empirical study that compares the behavior of both global and local search-based optimization on real-world programs. The results of this study reveal that cases exist of test data generation problem that suit each algorithm, thereby suggesting that a hybrid global-local search (a Memetic Algorithm) may be appropriate. The paper presents a Memetic Algorithm along with further empirical results studying its performance.

Using Dominance for Aiding the Search-Based Testing to Overcome the Limitations of the Control-Dependencies

Search-based optimization techniques have been utilized for a number of software engineering activities. The representation of the problem and the definition of the fitness function ( FF ) are two key ingredients for the application of search-based optimization to software engineering problems. Therefore, a well-defined fitness function is essential to the effectiveness and efficiency of the search-based testing ( SBT ). Several search based test-data generation techniques have been developed. A wide range of these techniques utilized the control dependencies ( CD ) in the control-flow graph of the program under test for guiding the search in the direction of finding test data. To direct the SBT to generate test data, Ghiduk et al. have presented a search-based technique that utilizes the dominances ( Dom ) between the nodes of the control-flow graph ( CFG ) of the program under test. In this paper, we investigate the efficiency and effectiveness of dominances in a control-flow graph against the control dependencies in guiding the SBT for generating test data. The paper provides a number of structures programming which challenge the SBT that is guided by the control dependencies to find test data. The paper introduces two schemes for overcoming these problems. The first scheme improves the definitions of the fitness functions of the previous work to overcome the control-dependencies problems. The second scheme presents a general form for a fitness function in terms of dominances and postdominances nodes. This function enhances significantly the efficiency of the SBT ; consequently the SBT overcomes the control-dependencies problems. In addition, the paper compares between the efficiency of dominances and control dependencies in guiding SBT with proper examples from the literatures.

Fast Multidisciplinary Design Optimization via Taguchi Methods and Soft Computing

It is difficult to perform multidisciplinary design optimization using traditional searchbased optimization techniques due to possible conflicts among objectives from different disciplines,thetimeconsumingsearch,andthepossibilityofdivergence.Toovercomethedifficulties, this paper presents simulation-based design optimization techniques using Taguchi methods and soft computing (i.e. fuzzy logic and neural networks). An aircraft engine cycle design optimization with four conflicting design objectives is used to validate the presented approach. The result shows significant performance improvement in optimizing single and multiple design objectives. HE emerging field of Multidisciplinary Design Optimization (MDO) seeks to improve design methodology to rapidly and efficiently explore multiple-dimension design spaces with the goal of increasing system performance significantly, thereby reducing end-product cost substantially. Search-based and simulation-based are the two major system design approaches. The former is traditional and mathematical, and has existed for a long time. The optimum solution has to do with the selected starting point, and the optimization method used. A possibility of divergence in solution seeking is a major drawback in this approach. In contrast, the simulation-based approach uses the analysis and evaluation of a candidate solution, and the assessment of the degree to which the candidate satisfies the requirement. This optimum design tool uses the simulation-based approach. 1 With this new approach, the optimum solution can be obtained in real time. The traditional search-based optimization is a typical example of hard computing. In hard computing, the prime desiderata are precision, certainty and vigor. In contrast, in soft computing the principal notion is that precision and certainty carry a cost; and that computation, reasoning, and decision-making should exploit (whenever possible) the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for obtaining low cost solutions. Fuzzy logic and neural networks, the two major soft computing techniques, have very contrasting application requirements. Fuzzy systems are appropriate if sufficient expert knowledge about the process is available, while neural systems are useful if sufficient data are available or measurable. Furthermore, neural networks possess the ability to learn the input-output relationship. A trained neural network provides instantly input-to-output mapping with reasonably good accuracy, but without knowledge representation. Fuzzy logic, on the other hand, possesses the ability for knowledge representation and inference, but has no capabilities for automated learning. Thus, fuzzy logic and neural networks compensate each other in terms of information processing.