Search-based Optimization Algorithms Research Articles

A novel multi-objective learning-to-rank method for software defect prediction

Search-Based Software Engineering (SBSE) is one of the techniques used for software defect prediction (SDP), in which search-based optimization algorithms are used to identify the optimal solution to construct a prediction model. As we know, the ranking methods of SBSE are used to solve insufficient sample problems, and the feature selection approaches of SBSE are employed to enhance the prediction model?s performance with curse-of-dimensionality or class imbalance problems. However, it is ignored that there may be a complex problem in the process of building prediction models consisting of the above problems. To address the complex problem, two multi-objective learning-to-rank methods are proposed, which are used to search for the optimal linear classifier model and reduce redundant and irrelevant features. To evaluate the performance of the proposed methods, excessive experiments have been conducted on 11 software programs selected from the NASA repository and AEEEM repository. Friedman?s rank test results show that the proposed method using NSGA-II outperforms other state-of-the-art singleobjective methods for software defect prediction.

Optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects

The selective withdrawal system (SWS) has often been used to alleviate downstream temperature pollutions and in-reservoir unacceptable oxygen concentrations whilst increasing hydropower energy generation. The aim of this study is to a) design the optimal locations of intakes in SWS, and, b) derive optimal parameterized selective withdrawal operation rules. The integration of water quality and quantity aspects to meet long-term environmental services and economic benefits is challenging. The main challenge lies in coupling search-based optimization algorithms with a numerical hydrodynamic and water quality simulation model (i.e., CE-QUAL-W2). To cope with the computational burdens of CE-QUAL-W2, surrogate models have been developed to represent the dynamics of temperature and water quality according to various SWSs. The surrogate models and the hydropower energy computation module were coupled with a multi-objective particle swarm optimization algorithm in an adaptive surrogate-based simulation–optimization framework (ASBSOF). The ASBSOF is applied in Karkheh reservoir, Andimeshk, Khuzestan, Iran, to address problems a and b. The results indicate that the optimal SWSs provide the advantage of sustainable management to balance the energy generation whilst minimizing environmental damages.

GBUO: “The Good, the Bad, and the Ugly” Optimizer

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms.

A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

Regression testing is the essential process of software maintenance and evolution phase of the software development life cycle for assuring the quality and reliability of updated software. Test case prioritization is the technique of regression testing to reduce the time and effort required for regression testing. Search-based algorithms are used to enhance the efficiency and effectiveness of the method. Among these search-based optimization algorithms, genetic algorithms are becoming more popular among researchers since the last decade. In this paper, we are doing a systematic literature review, i.e., a secondary study of test case prioritization using genetic algorithms. The objective of this review is to examine and classify the current state of use of the genetic algorithm in test case prioritization. In other words, to give a base for the advancement of test case prioritization research using genetic algorithms. With the use of the systematic literature review protocol, we selected the most relevant studies (20 out of 384) from the appropriate repositories by using a set of search keywords, inclusion/exclusion criteria and the quality assessment of studies. The data extraction and synthesis process and the taxonomic classification are used to answer the research questions. We also performed a rigorous analysis of the techniques by comparing them on research methodology, the prioritization method, dataset specification, test suite size, types of genetic algorithms used, performance metrics, and the validation criteria. The whole process took four months for comprehensive analysis and classification of primary studies. We observed that the parameter settings, the type of operators, the probabilistic rate of operators, and fitness function design have a significant impact on the quality of the solutions obtained. This systematic literature review yields that genetic algorithms have great potential in solving test case prioritization problems, and the area is open for further improvements. Future researchers can fill the research gaps by following the suggestions given in the review. From this review, we found that the use of the appropriate approach can make a genetic algorithm based test case prioritization one of the effective methods in regression testing.

Factored Evolutionary Algorithms

Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function. In this paper, we give a formal definition of FEA algorithms and present empirical results related to their performance. One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize. For this reason, we present the results of experiments comparing the performance of different factor architectures on several standard applications for evolutionary algorithms. Additionally, we show that FEA’s performance is not restricted by the underlying optimization algorithm by creating FEA versions of hill climbing, particle swarm optimization, genetic algorithm, and differential evolution and comparing their performance to their single-population and cooperative coevolutionary counterparts.

Layout optimization for maximizing wind farm power production using sequential convex programming

This paper describes an efficient method for optimizing the placement of wind turbines to maximize the expected wind farm power. In a wind farm, the energy production of the downstream wind turbines decreases due to reduced wind speed and increased level of turbulence caused by the wakes formed by the upstream wind turbines. As a result, the wake interference among wind turbines lower the overall power efficiency of the wind farm. To improve the overall efficiency of a wind farm, researchers have studied the wind farm layout optimization problem to find the placement locations of wind turbines that maximize the expected wind farm power. Most studies on wind farm layout optimization employ heuristic search-based optimization algorithms. In spite of their simplicity, optimization algorithms based on heuristic search are computationally expensive and have limitation in optimizing the locations of a large number of wind turbines since the computational time for the search tends to increase exponentially with increasing number of wind turbines. This study employs a mathematical optimization scheme to efficiently and effectively optimize the locations of a large number of wind turbines with respect to maximizing the wind farm power production. To formulate the mathematical optimization problem, we derive a continuous wake model and express the expected wind farm power as a continuous and smooth function in terms of the locations of the wind turbines. The constructed wind farm power function is then maximized using sequential convex programming (SCP) for the nonlinear mathematical problem. We show how SCP can be used to evaluate the efficiency of an existing wind farm and to optimize a wind farm layout consisting of 80 wind turbines.

Search-based software engineering

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article 1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

Using search based optimization algorithms in Bridge Weigh-In-Motion systems

A method for vehicle axle load identification on slab-on-girder bridges is presented in this paper. The method's basis lies in developing a bridge specific static influence line matrix and using an optimization method combined with a pattern search algorithm. A 1/3 scale laboratory model of a six girder bridge was used as part of the case studies demonstrating methodology development and implementation. A finite element bridge model was developed and calibrated against experimental data. The search based optimization procedure was needed to estimate vehicle characteristics when noise was present in the recorded data and when error was present in the numerical model. The actual vehicle and the estimated vehicle characteristics therefore did not exactly match - a deviation designated as an identification error. The identification error's statistical behavior with respect to the level of noise in the measured response or error in the model was studied. Methods for identification error reduction were numerically evaluated. The importance of a field calibrated bridge model was shown, as was that averaging multiple vehicle estimates for different positions on the bridge can decrease the identification errors.

Neighbourhood search-based optimization algorithms for production scheduling: a survey

Neighbourhood search is one of the general strategies used in designing heuristic algorithms for discrete optimization. Apart from its simplicity from the conceptual and implementation point of view, a notable characteristic of neighbourhood search is its generality: no assumption is made about the objective and the constraints, whereas other heuristic methods depend on the particular problem at hand. Neighbourhood search is, to say the least, mathematically unexciting, and for many problems specific heuristic algorithms exist with better performance. However, from a practical point of view, the ease of conception and implementation of a neighbourhood search algorithm make it a most interesting candidate for the quick prototyping of optimization software for many domains, including manufacturing. These characteristics have justified the continuous interest in neighbourhood search. Some algorithms have been proposed to overcome the greatest shortcoming of neighbourhood search, i.e. the tendency to get stuck in a local minimum. In this paper the two most interesting neighbourhood search-based algorithms, simulated annealing and tabu search, are presented and evaluated by comparing them with an exact algorithm for a simple scheduling problem. Due to the complexity of optimization problems encountered in the CIM world, the practitioner will find these algorithms a most useful tool.