Test Suite Research Articles

Balancing exploration and exploitation in dynamic constrained multimodal multi-objective co-evolutionary algorithm

Constrained multimodal multi-objective optimization (CMMOPs) involves multiple equivalent constrained Pareto optimal sets (CPSs) matching the same constrained Pareto front (CPF). An essential challenge in solving CMMOPs is how to balance exploration and exploitation in searching for the CPSs. To tackle this issue, a dynamic constrained co-evolutionary multimodal multi-objective algorithm termed DCMMEA is developed in this paper. DCMMEA involves a constraint-relaxed population for handling constraints and a convergence-relaxed population for improving convergence quality. Subsequently, a constraint-relaxed epsilon strategy that considers the constraint violation degree between individuals is designed and applied dynamically in the constraint-relaxed population to develop equivalent CPSs. Similarly, a dynamic convergence-relaxed epsilon strategy that considers the differences between objective values is developed and used dynamically in the convergence-relaxed population. It explores CPSs with high convergence quality and transfers the convergence knowledge to the constraint-relaxed population. Additionally, the constraint- relaxed population size is dynamically increased and the convergence-relaxed population size is dynamically decreased to balance the exploration and exploitation procedures. Experiments are performed on standard CMMOP test suites and validate that DCMMEA obtains superior performance on solving CMMOPs in comparison to state-of-the-art algorithms. Also, DCMMEA is implemented on standard CMOPs and demonstrated good performance in handling CMOPs.

LiteHash: Hash Functions for Resource-Constrained Hardware

The global paradigm shift towards edge computing has led to a growing demand for efficient integrity verification. Hash functions are one-way algorithms which act as a zero-knowledge proof of a datum’s contents. However, it is infeasible to compute hashes on devices with limited processing power and memory. Hence, we propose four novel LiteHash functions which are architecturally similar to SHA-512 yet simpler. By using various approximation techniques, our implementations reduce the computational costs of digesting a message into a hash. On validating our proposed designs using the NIST PRNG Test Suite, we observe SHA-512 equivalent cryptographic security while satisfying all desired hash function property requirements. We observe a minimum of 9.41% reduction in area, 20.47% reduction in power and 22.05% increase in throughput. Our designs offer a throughput of upto 2 Gbps while reducing area and power by a maximum of 16.86% and 32.48% respectively. LiteHash functions also support the computation of the entire SHA-2 family of hash functions (SHA-224/256/384/512) with minor architectural modifications.

A coevolutionary algorithm using Self-organizing map approach for multimodal multi-objective optimization

Multimodal Multi-Objective Problems (MMOPs) are frequently encountered in the real world. Traditional Multimodal Multi-Objective Evolutionary Algorithms (MMEAs) often find multiple Pareto optimal solutions with the same objective values. However, in real-world problems, there often exist multiple global optimal solutions and local optimal solutions at the same time. Ensuring that these solution sets are obtained simultaneously is the concern of most current researchers. To address this issue, this paper proposes a novel multimodal multi-objective evolutionary algorithm named CoSOMEA. In the CoSOMEA, a Self-organizing map (SOM) neural network is used to extract the information of decision space to ensure better exploration of the global optima and exploitation of the local optima. Meanwhile, coevolutionary mechanism are used to ensure a balance between the exploration and exploitation in order to avoid the algorithm falling into local areas. The three test suites named IDMP, IDMP_ee and MMF are adopted to verify the effectiveness of proposed algorithm. Experimental results demonstrate that the CoSOMEA exhibits competitive performance in solving MMOPs compared to other state-of-the-art MMEAs.

An Improved Football Team Training Algorithm for Global Optimization.

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.

Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimization

Surrogate-Assisted Evolutionary Algorithms (SAEAs) integrate Evolutionary Algorithms (EAs) with surrogate models to reduce the actual number of expensive function evaluations and have been widely used in solving Expensive Optimization Problems (EOPs). However, because of the insufficient diversity of new solutions, SAEAs often fall into local optima and result in evolutionary stagnation when dealing with complex problems. To improve the diversity of new solutions, this paper introduces a Surrogate-Assisted evolutionary Framework with an ensemble of Teaching-learning and Differential evolution (SAF-TD), which successfully integrates the strengths of both EAs to produce more diverse and effective solutions. The main contributions are summarized as follows: First, a novel framework that effectively integrates two distinct EAs using a radial basis function surrogate model and strategic sampling is introduced. Second, a mutation strategy with a supervisory mechanism is proposed to enhance mutation effectiveness and avoid stagnation. Third, a volatility index derived from dimensional improvements is incorporated into parameter control to address fitness-dependent weaknesses. Experiments on five expensive optimization functions and the CEC2017 test suite across multiple dimensions were conducted to evaluate the performance of SAF-TD. The results demonstrate that SAF-TD is highly competitive compared with state-of-the-art SAEAs and exhibits excellent performance in solving EOPs.

Smoking affects global and regional brain entropy in depression patients regardless of depression: Preliminary findings

ObjectiveThis study examines the effect of smoking on global and regional brain entropy in patients with Major Depressive Disorder (MDD), aiming to elucidate the relationship between smoking habits and brain network complexity in depression. MethodsThe study enrolled 24 MDD patients, divided into smokers and non-smokers, from Alanya Alaaddin Keykubat University and Istanbul Medipol University. Resting-state fMRI data were acquired and processed. The complexity of neuronal activity was assessed using dispersion entropy, with statistical significance determined by a suite of tests including Kolmogorov–Smirnov, Student's t-test, and Mann–Whitney U test. ResultsThe smoking cohort exhibited higher global brain entropy compared to the non-smoking group (p = 0.033), with significant differences in various brain networks, indicating that smoking may alter global brain activity and network dynamics in individuals with MDD. ConclusionThe study provides evidence that smoking is associated with increased brain entropy in MDD patients, suggesting that chronic smoking may influence cognitive and emotional networks. This underscores the importance of considering smoking history in the treatment and prognosis of MDD. The findings call for further research to understand the mechanistic links between smoking, brain entropy, and depression.

Evaluating the effectiveness of minimum chlorate technologies employed by manufacturers of skim milk powder from both microbiological quality and chemical residue perspectives

Dairy processors in the Republic of Ireland have adopted chlorine-free chemicals for cleaning and chlorine gas for water disinfection as a means of minimizing chlorate residue in dairy products. For these 'minimum chlorate technologies' to be satisfactory, they must be able to deliver product with acceptable levels of bacteria as well as minimum levels of chlorate and other chlorine based residues. To establish the effectiveness of these technologies, sampling was conducted across the skim milk powder (SMP) manufacturing chain in 3 separate milk processing sites. Across the 3 sites a total of 11 different batches of SMP were sampled in duplicate from the whole milk silo through the manufacturing process to the powder product; yielding a total of 137 samples. Samples were tested for chlorate, perchlorate and trichloromethane alongside a suite of microbiological plate count tests including total bacteria, thermophilic bacteria, thermoduric bacteria and both mesophilic and thermophilic spore-forming bacteria. Chlorate was detected at reportable levels (≥0.01 mg/kg) in 9 of 22 SMP samples analyzed; resulting in a mean chlorate concentration 0.0183 mg/kg. Bacteria were ubiquitous across all samples analyzed with spore-forming bacteria counts ranging from 1.30 to 2.33 log cfu/ g in SMP.

Quantum Chimp Optimization Algorithm: A Novel Integration of Quantum Mechanics Into the Chimp Optimization Framework for Enhanced Performance

Abstract This research introduces the Quantum Chimp Optimization Algorithm (QChOA), a pioneering methodology that integrates quantum mechanics principles into the Chimp Optimization Algorithm (ChOA). By incorporating non-linearity and uncertainty, the QChOA significantly improves the ChOA’s exploration and exploitation capabilities. A distinctive feature of the QChOA is its ability to displace a ’chimp,’ representing a potential solution, leading to heightened fitness levels compared to the current top search agent. Our comprehensive evaluation includes twenty- nine standard optimization test functions, thirty CEC-BC functions, the CEC06 test suite, ten real-world engineering challenges, and the IEEE CEC 2022 competition’s dynamic optimization problems. Comparative analyses involve four ChOA variants, three leading quantum-behaved algorithms, three state-ofthe-art algorithms, and eighteen benchmarks. Employing three non-parametric statistical tests (Wilcoxon rank-sum, Holm-Bonferroni, and Friedman average rank tests), results show that the QChOA outperforms counterparts in 51 out of 70 scenarios, exhibiting performance on par with SHADE and CMA-ES, and statistical equivalence to jDE100 and DISHchain1e+12. The study underscores the QChOA’s reliability and adaptability, positioning it as a valuable technique for diverse and intricate optimization challenges in the field.

Extraction and empirical evaluation of GUI-level invariants as GUI Oracles in mobile app testing

ContextMobile apps (software) are used in almost all aspects of daily life by billions of people. Given the widespread use of mobile apps in various domains, the demand for systematic testing of their Graphical User Interfaces (GUI) is crucial. Despite the significant advances in automated mobile app testing over the last decade, certain challenges remain, most notably the app-specific GUI test-oracle problem, which can significantly hinder the effective detection of defects in mobile apps. In this study, we introduce the use of GUI-level invariants, referred to as GUI invariants, as app-specific GUI oracles in GUI test cases to address this challenge. MethodsWe propose a semi-automatic solution to extract GUI invariants and use them as app-specific GUI oracles in test cases. We use the mutation testing technique to evaluate the (fault detection) effectiveness of the GUI oracles used. In addition, we evaluate their quality aspects, namely correctness, understandability, and compatibility, from the perspective of human experts using a questionnaire survey. ResultsThe empirical results show that the GUI oracles used are effective and helpful, as they improved the fault-detection effectiveness of the empirical test suites ranging from 18% to 32%. These results also highlight the efficacy of GUI oracles used in identifying various defects, including crashing and non-crashing functional issues, and surpassing the performance of existing tools in fault-detection rates. Additionally, the questionnaire survey outcomes indicate that the GUI oracles used are correct, understandable, and compatible. ConclusionsBased on the empirical results, we can conclude that using GUI invariants as GUI oracles can be useful and effective in mobile app testing.

A novel preference-driven evolutionary algorithm for dynamic multi-objective problems

Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 30 widely used benchmark problems in which it outperforms contrasting algorithms on 71% of test suits. Empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs.

A Stealthy Communication Model with Blockchain Smart Contract for Bidding Systems

With the widespread adoption of blockchain technology, its public ledger characteristic enhances transaction transparency but also amplifies the risk of privacy breaches. Attackers can infer users’ real identities and behaviors by analyzing public transaction patterns and address relationships, posing a severe threat to users’ privacy and security, and thus hindering further advancements in blockchain applications. To address this challenge, covert communication has emerged as an effective strategy for safeguarding the privacy of blockchain users and preventing information leakage. But existing blockchain-based covert communication schemes rely solely on the immutability of blockchain itself for robustness and suffer from low transmission efficiency. To tackle these issues, this paper proposes a stealthy communication model with blockchain smart contract for bidding systems. The model initiates by preprocessing sensitive information using a secret-sharing algorithm-the Shamir (t, n) threshold scheme-and subsequently embeds this information into bidding amounts, facilitating the covert transfer of sensitive data. We implemented and deployed this model on the Ethereum platform and conducted comprehensive performance evaluations. To assess the stealthiness of our approach, we employed a suite of statistical tests including the CDF, the Kolmogorov–Smirnov test, Welch’s t-test and K–L divergence. These analyses confirmed that amounts carrying concealed information were statistically indistinguishable from regular transactions, thus validating the effectiveness of our solution in maintaining the anonymity and confidentiality of information transmission within the blockchain ecosystem.

ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiency

Differential Evolution (DE) is a simple but powerful population based evolutionary algorithm, which was widely used to solve various complex optimization problems. However, even recently proposed state-of-the-art DE variants tend to get trapped in local minima due to insufficient population diversity in the later stages of evolution. In this paper, an Adaptive Cluster Division Differential Evolution (ACD-DE) algorithm was proposed to mitigate population diversity deficiency. The main highlights are summarized as follows: Firstly, a novel cluster division based mutation strategy was proposed to enhance the diversity of difference vectors during the mutation operation. Secondly, a population diversity detection indicator was used to assess the stagnation level of individuals, enabling stagnation management to be launched based on different stagnation levels. Thirdly, an effective evolution guidance mechanism was proposed by adjusting certain parameters in the D-dimensional vectors of outlier individuals that significantly deviate from the current population. Fourthly, novel parameter adaptations were employed to dynamically adjust the control parameters, including the scale factor F, crossover rate CR, and population size PS, during evolution. To validate the ACD-DE algorithm, extensive experiments were conducted on 88 benchmark functions from the CEC2013, CEC2014, and CEC2017 test suites, focusing on the optimization accuracy, convergence speed, time complexity, and component effectiveness. The results demonstrate the superiority of our ACD-DE algorithm compared to state-of-the-art DE variants.

A staged diversity enhancement method for constrained multiobjective evolutionary optimization

Optimizing the convergence and diversity of solutions simultaneously under constraints is a challenge in solving constrained multiobjective optimization problems. In existing multiobjective optimization algorithms, general diversity maintenance mechanisms have difficulty determining all optimal solutions in discrete feasible regions. This paper proposes a staged constrained multiobjective optimization algorithm with a diversity enhancement method (SDEM), which can explore potential discrete feasible regions by retaining well-distributed offspring. Specifically, after solutions have converged to optimal feasible regions by niching-based constraint dominance in the early stage, the SDEM improves the diversity of solutions through a proposed diversity enhancement dominance principle in the mid-term. Finally, the optimize objective functions and constraints of all solutions are optimized under constraint dominance to balance convergence, diversity, and feasibility during the three stages. Experiments on four well-known test suites and six real-world case studies demonstrate that the SDEM is competitive with or comparable to seven state-of-the-art constrained multiobjective evolutionary algorithms.

Modified dung beetle optimizer with multi-strategy for uncertain multi-modal transport path problem

Abstract Uncertain multi-modal transport path optimization (UMTPO) is a combined optimization non-deterministic polynomial-time hard problem. Its goal is to determine a path with the lowest total transportation cost and carbon emissions from the starting point to the destination. To effectively address this issue, this article proposes a modified dung beetle optimizer (DBO) to address it. DBO is a swarm-based metaheuristic optimization algorithm that has the features of a fast convergence rate and high solution accuracy. Despite this, the disadvantages of weak global exploration capability and falling easily into local optima exist. In this article, we propose a modified DBO called MSHDBO for function optimization and to solve the UMTPO problem. However, for the vast majority of metaheuristic algorithms, they are designed for continuous problems and cannot directly solve discrete problems. Therefore, this article employs a priority based encoding and decoding method to solve the UMTPO problem. To verify the performance and effectiveness of the MSHDBO algorithm, we compared it with other improved versions of the DBO algorithm used in the literature. We confirmed the excellent performance of MSHDBO using 41 benchmark test functions from the IEEE CEC 2017 test suite and IEEE CEC 2022 test suite. Additionally, we compared the MSHDBO algorithm with 10 other state-of-the-art metaheuristic optimization algorithms through a practical UMTPO problem. The experimental results indicated that the MSHDBO algorithm achieved very good performance when solving the UMTPO problem.

Measuring and improving software testability at the design level

ContextThe quality of software systems is significantly influenced by design testability, an aspect often overlooked during the initial phases of software development. The implementation may deviate from its design, resulting in decreased testability at the integration and unit levels. ObjectiveThe objective of this study is to automatically identify low-testable parts in object-orientated design and enhance them by refactoring to design patterns. The impact of various design metrics mainly coupling (e.g., fan-in and fan-out) and inheritance (e.g., depth of inheritance tree and number of subclasses) metrics on design testability is measured to select the most appropriate refactoring candidates. MethodThe methodology involves creating a machine learning model for design testability prediction using a large dataset of Java classes, followed by developing an automated refactoring tool. The design classes are vectorized by ten design metrics and labeled with testability scores calculated from a mathematical model. The model computes testability based on code coverage and test suite size of classes that have already been tested via automatic tools. A voting regressor model is trained to predict the design testability of any class diagram based on these design metrics. The proposed refactoring tool for dependency injection and factory method is applied to various open-source Java projects, and its impact on design testability is assessed. ResultsThe proposed design testability model demonstrates its effectiveness by satisfactorily predicting design testability, as indicated by a mean squared error of 0.04 and an R2 score of 0.53. The automated refactoring tool has been successfully evaluated on six open-source Java projects, revealing an enhancement in design testability by up to 19.11 %. ConclusionThe proposed automated approach offers software developers the means to continuously evaluate and enhance design testability throughout the entire software development life cycle, mitigating the risk of testability issues stemming from design-to-implementation discrepancies.

Two-stage particle swarm optimization with dual-indicator fusion ranking for multi-objective problems

Elite solutions guiding population evolution are often used as one of main ideas to improve the performance of multi-objective particle swarm optimization (MOPSO). However, in most research work, sole Pareto dominance criterion is often used to evaluate solutions. This sole criterion may easily cause some problems, such as the premature convergence. In this study, we propose an MOPSO variant with dual-indicator fusion ranking (TPSO-DF), to evaluate elite solutions and to guide search without sacrificing diversity. In TPSO-DF, two indicators are introduced by using the convergence and diversity information, respectively. Both indicators are then fusioned in a ranking measure to focus on valuable information and to filter out solutions with these valuable information. Meanwhile, an adaptive global leader selection strategy is introduced to take full advantage of valuable information and to guide population evolution toward the optimal direction. As another contribution of this study, a two-stage hybrid mutation strategy is designed by utilizing the valuable information differently in different evolutionary states of the algorithm to enhance performance. Compared to eight representative multi-objective evolutionary algorithms, the performance of TPSO-DF is validated by extensive experiments on ZDT and DTLZ test suites, as well as one practical problem. Experimental results show that TPSO-DF can achieve competitive performance on most of the test functions.

X-ray-driven multi-bit quantum random number generator.

Random numbers are vital in cryptography, simulation modeling, and gambling. This study presents a scheme for a multi-bit quantum random number generator utilizing X-ray radiation. Using a homemade multi-pixel single-photon detector array, we extract randomness from three modes of X-ray radiation: arrival time, spatial position, and polarization direction. We employ the Toeplitz Matrix Hashing extractor for randomness distillation, resulting in the extraction of 40.4 million random bits at a rate of 33.7 bits per photon. The generated random numbers pass all test criteria in the National Institute of Standards and Technology statistical test suite. Our work paves what we believe to be a novel method for generating multi-bit quantum random numbers, promising enhanced security and reliability in various technological applications.

Slime mould algorithm with mechanism of leadership and self-phagocytosis for multilevel thresholding of color image

Threshold based segmentation method is widely used because of its simplicity, high efficiency and easy implementation. However, as the number of thresholds increases, the algorithm's search space also increases, increasing computational complexity, which reduces computational efficiency and segmentation accuracy. Additionally, the existing multi-threshold (MT) segmentation methods still struggle with slow convergence speed and low solution accuracy. The optimum threshold for the image is determined in this study using the Slime Mould Algorithm with the Mechanism of Leadership and Self-phagocytosis (SMA-MLS). Firstly, the initial population production method based on Logistic-Tent mapping is introduced to enhance the diversity of algorithm solutions. Secondly, the position update formula with leadership mechanism is proposed to improve the convergence speed and accuracy. In addition, the adaptive combined mutation mechanism balances the exploration and exploitation ability. Finally, the self-phagocytosis mechanism maintains population diversity and heritability. A series of benchmark test suites are employed to evaluate the performance of the algorithm. Experimental results demonstrate that SMA-MLS does show good performance. It is superior in terms of convergence accuracy, convergence speed and stability. In the MT segmentation experiments, by using Kapur as the objective function and working with color images, SMA-MLS proved to be effective in the MT segmentation problem of images. Moreover, SMA-MLS will be applied to mechanical parameter optimization, neural network and other fields in future research.

Niche center identification differential evolution for multimodal optimization problems

Niching techniques are commonly incorporated into evolutionary computation (EC) algorithms to address multimodal optimization problems (MMOPs). Nevertheless, identifying proper individuals as niche centers remains the main challenge in niching techniques. Generally, niche centers should possess promising fitness (fitness aspect) and should be dispersedly distributed in different search regions (distance aspect). In this study, we propose a novel niching technique known as niche center identification (NCI) and integrate it with differential evolution (DE) for tackling MMOPs, termed NCIDE. In NCI, niche centers are first identified from both the fitness and distance aspects. Individuals that are not niche centers are added to their nearest niche centers to form niches. Moreover, we develop a niche-level archival-adaptive parameter scheme (NAAPS) to adaptively adjust the parameters at the niche level and reduce their sensitivity. Meanwhile, with the help of an archive, we can preserve the identified optima and reinitialize stagnant individuals for further exploration. The experimental results on the CEC2013 multimodal benchmark test suite demonstrate that NCIDE significantly outperforms several state-of-the-art multimodal algorithms, including multiple competition winners from CEC2015 and GECCO2017-GECCO2019. Finally, NCIDE is applied to solve multimodal nonlinear equation system (NES) problems to further illustrate its practical applicability.

Nonlinear optimization of optical camera multiparameter via triple integrated Gradient-based optimizer algorithm

Metaheuristic techniques are widely used to solve various complex real-world problems. Camera calibration is a critical task in machine vision, which aims to determine the optical camera parameters through 2D image features and corresponding 3D spatial features. To overcome the limitations of traditional methods, such as weak local optima avoidance and low calibration accuracy, a triple integrated Gradient-based optimizer (TIGBO_CC) is proposed for nonlinear optimization of optical camera parameters. Several improvements are made to the original Gradient-based optimizer (GBO). First, in the exploration phase, a fitness-distance balance is adopted to select the most contributing candidate to guide the update of the solution. Second, a random search technique is adopted in the exploitation phase. The switching between the original mode and the additional mode follows the probability. Finally, a neighborhood local search is also introduced. Small-step fine search is performed near the optimal solution to improve the quality of the final solution. TIGBO_CC is evaluated on 12 classical functions, the CEC2020 test suite, and the CEC2022 test suite. The experiments include sensitivity analysis, effectiveness analysis, scalability analysis, function value, convergence curve, box plot, Friedman test, Wilcoxon test, etc. Furthermore, a nonlinear camera calibration model is established. Taking the mean reprojection error as the objective function, TIGBO_CC solves the optimal optical camera parameters by minimizing this function. In different scenarios, the calibration method based on TIGBO_CC has high precision and strong reproducibility. Comprehensive analysis shows that TIGBO_CC has excellent optimization ability and practicality.