Nature-inspired Search Algorithm Research Articles

Software Testing Using Cuckoo Search Algorithm with Machine Learning Techniques

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

Reliable prediction of software defects using Shapley interpretable machine learning models

Predicting defect-prone software components can play a significant role in allocating relevant testing resources to fault-prone modules and hence increasing the business value of software projects. Most of the current software defect prediction studies utilize traditional supervised machine learning algorithms to predict defects in software applications. The software datasets utilized in such studies are imbalanced and therefore the reported results cannot be reliably used to judge their performance. Moreover, it is important to explain the output of machine learning models employed in fault-predication techniques to determine the contribution of each utilized feature to the model output. In this paper, we propose a new framework for predicting software defects utilizing eleven machine learning classifiers over twelve different datasets. For feature selection, we employ four different nature-inspired search algorithms, namely, particle swarm optimization, genetic algorithm, harmony algorithm, and ant colony optimization. Moreover, we make use of the synthetic minority oversampling technique (SMOTE) to address the problem of data imbalance. Furthermore, we utilize the Shapley additive explanation model for highlighting the highest determinative features. The obtained results demonstrate that gradient boosting, stochastic gradient boosting, decision trees, and categorical boosting outperform others tested model with over 90% accuracy and ROC-AUC. Additionally, we found that the ant colony optimization technique outperforms the other tested feature extraction techniques.

Comparative Study of Evolutionary Computation Based PI, FOPI and NN Controllers for DSTATCOM

In this paper, nature inspired search algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA) are used to design fractional order proportional and integral (FOPI) and artificial neural network (ANN) controller based distribution static compensator (DSTATCOM) and electronic load controller (ELC) for power quality improvement. Improvement in power quality is achieved using DSTATCOM and an ELC. DSTATCOM is designed using FOPI and ANN based controllers, as opposed to conventional PI controllers which are comparatively less efficient. PSO and GA techniques are employed to determine the optimal parameters for the controllers. The improvement in the performance of the ANN and FOPI as compared to PI controller for the DSTATCOM and ELC is validated using MATLAB based modeling and simulations. Linear consumer loads were used to perform a comparative study in terms of maximum percentage error. Further, we analyzed the system for a nonlinear load and demonstrated decrease in the harmonic distortion.

Woodpecker Mating Algorithm (WMA): a nature-inspired algorithm for solving optimization problems

Nature-inspired metaheuristic algorithms have been a topic of interest for researchers to solve optimization problems in engineering designs and real-world applications, due to their simplicity and flexibility. This paper presents a new nature-inspired search algorithm called Woodpecker Mating Algorithm (WMA) and applies it to challenging problems in structural optimization. The WMA is a population-based metaheuristic algorithm that mimics the mating behavior of woodpeckers. It was inspired by the drumming sound intensity. In WMA, the population of woodpeckers is divided into male and female groups. The female woodpeckers approach the male woodpeckers based on the intensity of their drum sound. An efficiency comparison was drawn between the WMA algorithm and other metaheuristic algorithms by employing 19 benchmark functions(including unimodal, multimodal and composite functions). Moreover, the performance of WMA is compared with 8 of the best meta-heuristic algorithms using 13 high dimensional multimodal and unimodal benchmark functions. The assessments and statistical results indicate that the WMA algorithm offers promising results and is capable of outperforming the most recent and popular algorithms proposed in the literature in most of the employed benchmark functions. Moreover, a statistically significant difference was observed compared to the other assessed algorithms. The proposed algorithm produced significant results for a non-convex, inseparable, and scalable problems.

Teaching–learning-based optimisation algorithm and its application in capturing critical slip surface in slope stability analysis

The identification of ideal values for algorithm-specific parameters required for the functioning of metaheuristic approaches at their optimum performance is a difficult task. This paper presents the application of a recently proposed teaching–learning-based optimisation (TLBO) algorithm to determine the lowest factor of safety (FS) along a critical slip surface for soil slope. TLBO is a nature-inspired search algorithm based on the teaching–learning phenomenon of a classroom. Four benchmark slopes are reanalysed to test the performance of the TLBO approach. The results indicate that the present technique can detect the critical failure surface and can be easily implemented by practitioners without fine-tuning the parameters that affect the convergence of results. Statistical analyses indicate a drastic decrease in uncertainty and the number of function evaluations in the estimation of the FS over previous approaches.

The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

As a nature-inspired search algorithm, the Firefly algorithm (being a naturally outstanding search algorithm with few control parameters) may have a considerable influential performance. In this paper, we present a new firefly algorithm to address the parameter selection and adaptation strategy in the standard firefly algorithm. The proposed firefly algorithm introduces a modified exploration and exploitation mechanism, with adaptive randomness and absorption coefficients. The proposed method employs the adaptation of the randomness and absorption coefficients to be a function of time/iterations. Moreover, gray relational analysis advancing fireflies is used to allocate different information from appealing ones effectively. Standard benchmark functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of the proposed firefly algorithm is superior to (or at least highly competitive with) the standard firefly algorithm, and state-of-the-art approaches in terms of performance.

A Nonhomogeneous Cuckoo Search Algorithm Based on Quantum Mechanism for Real Parameter Optimization.

Cuckoo search (CS) algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this paper presents a new variant of CS algorithm with nonhomogeneous search strategies based on quantum mechanism to enhance search ability of the classical CS algorithm. Featured contributions in this paper include: 1) quantum-based strategy is developed for nonhomogeneous update laws and 2) we, for the first time, present a set of theoretical analyses on CS algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the CS algorithm and the proposed algorithm. On 24 benchmark functions, we compare our method with five existing CS-based methods and other ten state-of-the-art algorithms. The numerical results demonstrate that the proposed algorithm is significantly better than the original CS algorithm and the rest of compared methods according to two nonparametric tests.

Adaptive firefly algorithm: parameter analysis and its application.

As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm — adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem — protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

An improved harmony search algorithm for emergency inspection scheduling

The ability of nature-inspired search algorithms to efficiently handle combinatorial problems, and their successful implementation in many fields of engineering and applied sciences, have led to the development of new, improved algorithms. In this work, an improved harmony search (IHS) algorithm is presented, while a holistic approach for solving the problem of post-disaster infrastructure management is also proposed. The efficiency of IHS is compared with that of the algorithms of particle swarm optimization, differential evolution, basic harmony search and the pure random search procedure, when solving the districting problem that is the first part of post-disaster infrastructure management. The ant colony optimization algorithm is employed for solving the associated routing problem that constitutes the second part. The comparison is based on the quality of the results obtained, the computational demands and the sensitivity on the algorithmic parameters.

Multilevel Thresholding for Image Segmentation using the Galaxy-based Search Algorithm

In this paper, image segmentation of gray- level images is performed by multilevel thresholding. The optimal thresholds for this purpose are found by maximizing the between-class variance (the Otsu's criterion). The optimization (maximization) is conducted by a novel nature-inspired search algorithm, which is called Galaxy-based Search Algorithm or GbSA. The proposed GbSA is a metaheuristic for continuous optimization. It resembles the spiral arms of some galaxies to search for the optimal thresholds. The GbSA also uses a modified Hill Climbing algorithm as a local search. The GbSA also utilizes chaos for improving its performance, which is implemented by the logistic map. Experimental results show that the GbSA finds the optimal or very near optimal thresholds in all runs of the algorithm.

Optimizing nanophotonic cavity designs with the gravitational search algorithm

Designing photonic crystal cavities with high quality factors and low mode volumes is of great importance for maximizing interactions of light and matter in metamaterials. Previous work on photonic crystal cavities has revealed dramatic improvements in performance by fine-tuning the device design. In L3 cavities, slight shifts of the holes on the edge of the cavity have been found to greatly increase quality factors without significantly altering the mode volume. Here we demonstrate utilizing a nature inspired search algorithm to efficiently explore a large parameter space. The results converge upon a new cavity model with a high quality factor to mode volume ratio (Q/V = 798,000 (λ/n)(-3)).

Constraint-handling in nature-inspired numerical optimization: Past, present and future

In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented.

Search based software testing of object-oriented containers

Automatic software testing tools are still far from ideal for real world object-oriented (OO) software. The use of nature inspired search algorithms for this problem has been investigated recently. Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard. Because containers are used in almost every type of software, their reliability is of utmost importance. Hence, this paper focuses on the difficulties of testing container classes with nature inspired search algorithms. We will first describe how input data can be automatically generated for testing Java containers. Input space reductions and a novel testability transformation are presented to aid the search algorithms. Different search algorithms are then considered and studied in order to understand when and why a search algorithm is effective for a testing problem. In our experiments, these nature inspired search algorithms seem to give better results than the traditional techniques described in literature. Besides, the problem of minimising the length of the test sequences is also addressed. Finally, some open research questions are given.