Journal articles: 'TEST SUIT OPTIMIZATION'

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Mohapatra, Sudhir Kumar, and Srinivas Prasad. "Test Case Reduction Using Ant Colony Optimization for Object Oriented Program." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (December 1, 2015): 1424. http://dx.doi.org/10.11591/ijece.v5i6.pp1424-1432.

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Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

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Johari, Nur Farahlina, Azlan Mohd Zain, Noorfa Haszlinna Mustaffa, and Amirmudin Udin. "Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm." Applied Mechanics and Materials 815 (November 2015): 268–72. http://dx.doi.org/10.4028/www.scientific.net/amm.815.268.

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Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

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Liu, Yong, Wei-Min Zheng, Shangkun Liu, and Qing-Wei Chai. "Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks." Entropy 24, no. 8 (August 12, 2022): 1109. http://dx.doi.org/10.3390/e24081109.

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Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

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Liu, Jinguo, Yifan Luo, and Zhaojie Ju. "An Interactive Astronaut-Robot System with Gesture Control." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7845102.

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Human-robot interaction (HRI) plays an important role in future planetary exploration mission, where astronauts with extravehicular activities (EVA) have to communicate with robot assistants by speech-type or gesture-type user interfaces embedded in their space suits. This paper presents an interactive astronaut-robot system integrating a data-glove with a space suit for the astronaut to use hand gestures to control a snake-like robot. Support vector machine (SVM) is employed to recognize hand gestures and particle swarm optimization (PSO) algorithm is used to optimize the parameters of SVM to further improve its recognition accuracy. Various hand gestures from American Sign Language (ASL) have been selected and used to test and validate the performance of the proposed system.

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Koay, Ying-Ying, Jian-Ding Tan, Chin-Wai Lim, Siaw-Paw Koh, Sieh-Kiong Tiong, and Kharudin Ali. "An adaptive gravitational search algorithm for global optimization." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 724. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp724-729.

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<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

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You, Qi, Jun Sun, Vasile Palade, and Feng Pan. "Quantum-behaved particle swarm optimization with dynamic grouping searching strategy." Intelligent Data Analysis 27, no. 3 (May 18, 2023): 769–89. http://dx.doi.org/10.3233/ida-226753.

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The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC’2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.

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Ajani, Oladayo S., Abhishek Kumar, Rammohan Mallipeddi, Swagatam Das, and Ponnuthurai Nagaratnam Suganthan. "Benchmarking Optimization-Based Energy Disaggregation Algorithms." Energies 15, no. 5 (February 22, 2022): 1600. http://dx.doi.org/10.3390/en15051600.

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Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.

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Chai, Qing-Wei, and Jerry Wangtao Zheng. "Rotated Black Hole: A New Heuristic Optimization for Reducing Localization Error of WSN in 3D Terrain." Wireless Communications and Mobile Computing 2021 (October 11, 2021): 1–13. http://dx.doi.org/10.1155/2021/9255810.

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Wireless sensor network (WSN) attracts the attention of more and more researchers, and it is applied in more and more environment. The localization information is one of the most important information in WSN. This paper proposed a novel algorithm called the rotated black hole (RBH) algorithm, which introduces a rotated optimal path and greatly improves the global search ability of the original black hole (BH) algorithm. Then, the novel algorithm is applied in reducing the localization error of WSN in 3D terrain. CEC 2013 test suit is used to verify the performance of the novel algorithm, and the simulation results show that the novel algorithm has better search performance than other famous intelligence computing algorithms. The localization simulation experiment results reveal that the novel algorithm also has an excellent performance in solving practical problems. WSN localization 3D terrain intelligence computing rotated the black hole algorithm.

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Puphasuk, Pikul, and Jeerayut Wetweerapong. "An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization." Foundations of Computing and Decision Sciences 45, no. 2 (June 1, 2020): 97–124. http://dx.doi.org/10.2478/fcds-2020-0007.

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AbstractDesigning an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

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Chen, Z. J., Z. J. Cheng, and X. Q. Yan. "Multiobjective Optimization Problem of Multireservoir System in Semiarid Areas." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/354206.

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With the increasing scarcity of water resources, the growing importance of the optimization operation of the multireservoir system in water resources development, utilization, and management is increasingly evident. Some of the existing optimization methods are inadequate in applicability and effectiveness. Therefore, we need further research in how to enhance the applicability and effectiveness of the algorithm. On the basis of the research of the multireservoir system’s operating parameters in the Urumqi River basin, we establish a multiobjective optimization problem (MOP) model of water resources development, which meets the requirements of water resources development. In the mathematical model, the domestic water consumption is the biggest, the production of industry and agricultural is the largest, the gross output value of industry and agricultural is the highest, and the investment of the water development is the minimum. We use the weighted variable-step shuffled frog leaping algorithm (SFLA) to resolve it, which satisfies the constraints. Through establishing the test function and performance metrics, we deduce the evolutionary algorithms, which suit for solving MOP of the scheduling, and realize the multiobjective optimization of the multireservoir system. After that, using the fuzzy theory, we convert the competitive multiobjective function into single objective problem of maximum satisfaction, which is the only solution. A feasible solution is provided to resolve the multiobjective scheduling optimization of multireservoir system in the Urumqi River basin. It is the significance of the layout of production, the regional protection of ecological environment, and the sufficient and rational use of natural resources, in Urumqi and the surrounding areas.

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NARA, Hiroyuki, Takayuki Tanaka, Takashi Kusaka, Takayuki Yamagishi, and Shotaro OGURA. "2P1-C07 Support power optimization of smart suit using posture estimation for dairy operation in actual field test(Welfare Robotics and Mechatronics(3))." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _2P1—C07_1—_2P1—C07_2. http://dx.doi.org/10.1299/jsmermd.2013._2p1-c07_1.

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Page, Vincent, Christopher Dadswell, Matt Webster, Mike Jump, and Michael Fisher. "Towards the Determination of Safe Operating Envelopes for Autonomous UAS in Offshore Inspection Missions." Robotics 10, no. 3 (July 28, 2021): 97. http://dx.doi.org/10.3390/robotics10030097.

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A drive to reduce costs, carbon emissions, and the number of required personnel in the offshore energy industry has led to proposals for the increased use of autonomous/robotic systems for many maintenance tasks. There are questions over how such missions can be shown to be safe. A corollary exists in the manned aviation world for helicopter–ship operations where a test pilot attempts to operate from a ship under a range of wind conditions and provides subjective feedback on the level of difficulty encountered. This defines the ship–helicopter operating limit envelope (SHOL). Due to the cost of creating a SHOL there has been considerable research activity to demonstrate that much of this process can be performed virtually. Unmanned vehicles, however, have no test pilot to provide feedback. This paper therefore explores the possibility of adapting manned simulation techniques to the unmanned world to demonstrate that a mission is safe. Through flight modelling and simulation techniques it is shown that operating envelopes can be created for an oil rig inspection task and that, by using variable performance specifications, these can be tailored to suit the level of acceptable risk. The operating envelopes produced provide condensed and intelligible information regarding the environmental conditions under which the UAS can perform the task.

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Ullah, Kalim, Jiang Quanyuan, Guangchao Geng, Rehan Ali Khan, Sheraz Aslam, and Wahab Khan. "Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses." Energies 15, no. 9 (April 29, 2022): 3274. http://dx.doi.org/10.3390/en15093274.

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The number of microgrids within a smart distribution grid can be raised in the future. Microgrid-based distribution network reconfiguration is analyzed in this research by taking demand response programs and power-sharing into account to optimize costs and reduce power losses. The suggested method determined the ideal distribution network configuration to fulfil the best scheduling goals. The ideal way of interconnecting switches between microgrids and the main grid was also identified. For each hour of operation, the ideal topology of microgrid-based distribution networks was determined using optimal power flow. The results were produced with and without the use of a demand response program and power-sharing in each microgrid. Different load profiles, such as residential, industrial, commercial, and academic, were taken into account and modified using appropriate demand response programs and power-sharing using the Artificial Bee Colony algorithm. Various scenarios were explored independently to suit the diverse aims considered by the distribution network operator for improved observation. The ABC optimization in this research attempted to reduce the system’s total operation costs and power losses through efficient networked microgrid reconfiguration. The results of optimal microgrid topology revealed the effects of power-sharing and demand response (TOU) programs. The results obtained in the proposed idea shows that costs were reduced by 8.3% and power losses were reduced by 4%. The IEEE 33-bus test system was used to demonstrate the effectiveness of the proposed approach.

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Xu, Ying, Haijun Wang, Liying Zhang, Mingji Deng, Hechuan Jiang, Yaohua Guo, and Xu Yang. "Research on Bearing Capacity of Secant Piled-Bucket Foundation in Saturated Clay." Sustainability 14, no. 18 (September 14, 2022): 11511. http://dx.doi.org/10.3390/su141811511.

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The secant piled-bucket foundation (SPBF) is innovatively proposed to suit the large-capacity mainstream, which is optimized from a traditional foundation and consists of an upper pile cap and a lower bucket skirt. Compared with the pile foundation, the SPBF has great advantages and deserves further study. In this research, the bearing mode, bearing capacity and failure mode under various loads of SPBF in saturated clay have been fully studied. First, the small-scale model test in saturated clay is carried out to verify the finite element (FE) method; the deviation between the FE results and the test results under vertical load and horizontal–moment load is 10.65% and 10.25%, respectively. Next, the bearing mode of SPBF in engineering scales is investigated via FE method, the results indicating that the bearing mode of SPBF is similar to that of a prestressed tubular foundation. Finally, the bearing capacity and failure mode of SPBF are studied and the findings show that the vertical bearing capacity and horizontal–moment bearing capacity of SPBF is 96.53 MN and 1.62 MN, and the weak parts of SPBF are concrete of the pile cap and the anchor bolts, respectively. This paper provides support for design and further optimization in the future.

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Ma, Ming Tu, Gang Chen, Yu Hao Ma, Zhi Gang Li, and Yi Feng. "The Development and Application Research of Light Weight Heat Treated C-Grade Bullet Proof Steel." Advanced Materials Research 1063 (December 2014): 21–27. http://dx.doi.org/10.4028/www.scientific.net/amr.1063.21.

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The Light weight heat treated C-grade bullet proof steel was developed through composition design and optimization based on multiplex alloying, multiplex micro-alloying design ideas and complex phase structure strengthening theory. The puzzle how to avoid the quenching deformation problem of super high strength thin sheet was solved through heat treatment in die with a suit of cooling system. Such C-grade bullet proof steel plate has fine tempered lath martensite structure and has a higher strength than production which is made by a Inc. in Sweden. The shooting and certification test results show that the shot resistance of C-grade bullet proof steel plate can met the protection demand of the Protection specification for cash carrying vehicles (GA164-2005)standard. Comparison with C-grade bullet proof steel plate made by a Inc. in Sweden, the developed C-grade bullet proof steel plate can degrease 5 to 10% weight under the same shot resistance condition. It will be in favour of lightweight for cash truck and anti-hijacking vehicle and energy conservation and emission reduction.

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Jaśkiewicz, Marek, Damian Frej, Jan Matej, and Rafał Chaba. "Analysis of the Head of a Simulation Crash Test Dummy with Speed Motion." Energies 14, no. 5 (March 8, 2021): 1476. http://dx.doi.org/10.3390/en14051476.

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The article presents a model of an anthropometric dummy designed for low velocity crash tests, designed in ADAMS. The model consists of rigid bodies connected with special joints with appropriately selected stiffness and damping. The simulation dummy has the appropriate dimensions, shape, and mass of individual elements to suit a 50 percentile male. The purpose of this article is to draw attention to low speed crash tests. Current dummies such as THOR and Hybrid III are used for crash tests at speeds above 40 km/h. In contrast, the low-speed test dummy currently used is the BioRID-II dummy, which is mainly adapted to the whiplash test at speeds of up to 16km/h. Thus, it can be seen that there is a gap in the use of crash test dummies. There are no low-speed dummies for side and front crash tests, and there are no dummies for rear crash tests between 16 km/h and 25 km/h. Which corresponds to a collision of a passenger vehicle with a hard obstacle at a speed of 30 km/h. Therefore, in collisions with low speeds of 20 km/h, the splash airbag will probably not be activated. The article contains the results of a computer simulation at a speed of 20 km/h vehicle out in the ADAMS program. These results were compared with the experimental results of the laboratory crash test using volunteers and the Hybrid III dummy. The simulation results are the basis for building the physical model dummy. The simulation aims to reflect the greatest possible compliance of the movements of individual parts of the human body during a collision at low speed.

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Ogun, Basar, and Çigdem Alabas-Uslu. "Mathematical models for a batch scheduling problem to minimize earliness and tardiness." Journal of Industrial Engineering and Management 11, no. 3 (May 10, 2018): 390. http://dx.doi.org/10.3926/jiem.2541.

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Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem is addressed to provide on-time completion of customer orders in the environment of lean manufacturing. The problem is to optimize partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components.Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid from inventory of final products. The first model is a non-linear integer programming model while the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented.Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times comparing to the other two models. It was also showed that the alternative model can solve moderate sized real-world problems.Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature since it includes new circumstances which may arise in real-world applications. This research, also, contributes the literature of batch scheduling problem by presenting new optimization models.

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He, Long, Cheng Xu, and Xiaorong Guan. "Design Methodology and Experimental Study of a Lower Extremity Soft Exosuit." Electronics 12, no. 11 (June 1, 2023): 2502. http://dx.doi.org/10.3390/electronics12112502.

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Flexibility and light weight have become the development trends in the field of exoskeleton research. With high movement flexibility, low movable inertia and excellent wearable comfort, such a type of system is gradually becoming an exclusive candidate for applications such as military defense, rehabilitation training and industrial production. In this paper, aiming at assisting the walking of human lower limbs, a soft exosuit is investigated and developed based on the considerations of fabric structure, sensing system, cable-driven module, and control strategy, etc. Evaluation experiments are also conducted to verify its effectiveness. A fabric optimization of the flexible suit is performed to realize the tight bond between human and machine. Through the configuration of sensor nodes, the motion intention perception system is constructed for the lower limb exosuit. A flexible actuation unit with a Bowden cable is designed to improve the efficiency of force transmission. In addition, a position control strategy based on division of the gait phase is applied to achieve active assistance during plantar flexion of the ankle joint. Finally, to verify the assistive effectiveness of the proposed lower extremity exosuit, experiments including a physiological metabolic test and a muscle activation test are conducted. The experiment results show that the exosuit proposed in this paper can effectively reduce the metabolic consumption and muscle output of the human body. The design and methodology proposed in this paper can be extended to similar application scenarios.

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Gray, Johnnie, and Stefanos Kourtis. "Hyper-optimized tensor network contraction." Quantum 5 (March 15, 2021): 410. http://dx.doi.org/10.22331/q-2021-03-15-410.

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Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a variety of benchmarks, including the random quantum circuit instances recently implemented on Google quantum chips. We find that the paths obtained can be very close to optimal, and often many orders or magnitude better than the most established approaches. As different underlying geometries suit different methods, we also introduce a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding. The increase in quality of contraction schemes found has significant practical implications for the simulation of quantum many-body systems and particularly for the benchmarking of new quantum chips. Concretely, we estimate a speed-up of over 10,000× compared to the original expectation for the classical simulation of the Sycamore `supremacy' circuits.

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Altaie, Atica M., Tawfeeq Mokdad Tawfeeq, and Mustafa Ghanem Saeed. "Automated Test Suite Generation Tool based on GWO Algorithm." Webology 19, no. 1 (January 20, 2022): 3835–49. http://dx.doi.org/10.14704/web/v19i1/web19252.

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In succession, the size and complexity of the program increase and the scope of testing expand. So, to ensure deadline delivery and reduce development testing costs, program testing efficiency must be improved. Therefore, to ensure that the product is delivered on the deadline and the cost of testing development is reduced, the efficiency of the program's testing must be enhanced. In this study, highlighting is placed to generate test suite automatically to reach increase the coverage of paths based on two algorithms Grey Wolf Optimizer algorithm (GWO) and Particle swarm optimization (PSO). The results of the implementation will be compared with each other to demonstrate the performance and efficiency of the proposed methodology and the results show that the PSO algorithm has better than the GWO algorithm.

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Wu, Yuguo, Yulong Zhang, Jie Wang, Xiaoyu Zhang, Junfeng Wang, and Chunshan Zhou. "Study on the Effect of Extraneous Moisture on the Spontaneous Combustion of Coal and Its Mechanism of Action." Energies 13, no. 8 (April 16, 2020): 1969. http://dx.doi.org/10.3390/en13081969.

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It is imperative to have an in-depth understanding of the effect of extraneous moisture on the spontaneous combustion of coal not only for the control and prevention of coal spontaneous combustion in the coal mining industry, but also for the optimization design and application of the technological process. In this study, the type of moisture in a coal body has been redefined for the first time from the perspective of disaster prevention and control, i.e., original occurrence of moisture in the coal matrix and the extraneous moisture from the technological process. A suit of coal bodies with different extraneous moisture was prepared by soaking long-flame coal with a low water content. Using a temperature-programmed oxidation test, the effects of extraneous moisture on the temperature increase rate of coal bodies and the emission characteristics of gaseous products during coal spontaneous combustion were studied. Moreover, combined with the characterization of thermal analysis and of pore structure test, the action the mechanism of extraneous moisture on the coal spontaneous combustion process was also explored. The experimental results indicated that the effect of the extraneous moisture content varied with the development of coal spontaneous combustion. In the slow oxidation stage, extraneous moisture played a physical inhibition role in the coal oxidation. In the accelerated oxidation stage, extraneous moisture exhibited a catalytic effect on the coal–oxygen reaction or directly participated in the reaction. After entering the rapid oxidation stage, a delayed effect appeared. When the coal temperature exceeded 180 °C, the spontaneous combustion characteristics of coals with different initial moisture contents gradually tended to achieved balance.

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Bishnoi, Deepika, and Harsh Chaturvedi. "Optimal Design of a Hybrid Energy System for Economic and Environmental Sustainability of Onshore Oil and Gas Fields." Energies 15, no. 6 (March 11, 2022): 2063. http://dx.doi.org/10.3390/en15062063.

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The pollution caused by gas flaring is hazardous to the health of flora, fauna, and humans settled around the flaring site. Gas flaring also incurs economic loss as natural gas, an energy source, is wasted in flares. Furthermore, the unreliable electrical infrastructure is a roadblock for oil and gas companies attempting to achieve their production targets. This paper presents a framework to design hybrid energy systems (HES) which utilize the gas flare waste along with the locally available renewable energy sources to generate electricity. A novel dispatch strategy to suit the requirements of the oil and gas fields has been used for real-time simulations and optimization of the HES. As a test case, six different hybrid energy configurations, modelled for two gas flaring sites, Lakwa and Geleky in Assam—India, were analyzed and compared on the basis of economic and environmental factors. The best suitable configuration comprised 2000 kW solar photovoltaic (PV) panel sets, one 200 kW gas microturbine, two 30 kW gas microturbines, and grid connection. The proposed system economically outperformed the existing power system in the area by 35.52% in terms of the net present cost. Moreover, it could save 850 tons of carbon dioxide emissions annually, and it has a renewable fraction of 93.7% in the total energy generation. Owing to these merits, the presented technique would be a promising option for generation of electricity from flare gas waste and to mitigate pollution.

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Lindquist, Timothy E., Kurt M. Gutzmann, David L. Remkes, and Gary McKee. "Optimization of validation test suite coverage." ACM SIGSOFT Software Engineering Notes 16, no. 3 (July 1991): 87–92. http://dx.doi.org/10.1145/127099.127126.

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T., Ramasundaram. "TABU Search Prioritized Ant Colony Metaheuristic Optimization based Dynamic Symbolic Genetic Technique for High Coverage Test Suite Generation." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 324–36. http://dx.doi.org/10.5373/jardcs/v12sp1/20201079.

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Chetan J. Shingadiya et al.,, Chetan J. Shingadiya et al ,. "A Genetic Algorithm for Test Suite Optimization." International Journal of Computer Science Engineering and Information Technology Research 10, no. 1 (2020): 31–36. http://dx.doi.org/10.24247/ijcseitrjun20204.

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Singh, Shilpi, and Raj Shree. "Different Similarity Measures for Test Suite Optimization." Advanced Science, Engineering and Medicine 10, no. 7 (July 1, 2018): 833–36. http://dx.doi.org/10.1166/asem.2018.2234.

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Zhang, Zhiqiang, Jun Yan, Yong Zhao, and Jian Zhang. "Generating combinatorial test suite using combinatorial optimization." Journal of Systems and Software 98 (December 2014): 191–207. http://dx.doi.org/10.1016/j.jss.2014.09.001.

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Agrawal, Arun Prakash, Ankur Choudhary, and Arvinder Kaur. "An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm." International Journal of Distributed Systems and Technologies 11, no. 1 (January 2020): 53–67. http://dx.doi.org/10.4018/ijdst.2020010105.

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Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

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Anwar, Zeeshan, Ali Ahsan, and Cagatay Catal. "Neuro-Fuzzy Modeling for Multi-Objective Test Suite Optimization." Journal of Intelligent Systems 25, no. 2 (April 1, 2016): 123–46. http://dx.doi.org/10.1515/jisys-2014-0152.

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AbstractRegression testing is a type of testing activity, which ensures that source code changes do not affect the unmodified portions of the software adversely. This testing activity may be very expensive in, some cases, due to the required time to execute the test suite. In order to execute the regression tests in a cost-effective manner, the optimization of regression test suite is crucial. This optimization can be achieved by applying test suite reduction (TSR), regression test selection (RTS), or test case prioritization (TCP) techniques. In this paper, we designed and implemented an expert system for TSR problem by using neuro-fuzzy modeling-based approaches known as “adaptive neuro-fuzzy inference system with grid partitioning” (ANFIS-GP) and “adaptive neuro-fuzzy inference system with subtractive clustering” (ANFIS-SC). Two case studies were performed to validate the model and fuzzy logic, multi-objective genetic algorithms (MOGAs), non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms were used for benchmarking. The performance of the models were evaluated in terms of reduction of test suite size, reduction in fault detection rate, reduction in test suite execution time, and reduction in requirement coverage. The experimental results showed that our ANFIS-based optimization system is very effective to optimize the regression test suite and provides better performance than the other approaches evaluated in this study. Size and execution time of the test suite is reduced up to 50%, whereas loss in fault detection rate is between 0% and 25%.

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Karakasiliotis, K., R. Thandiackal, K. Melo, T. Horvat, N. K. Mahabadi, S. Tsitkov, J. M. Cabelguen, and A. J. Ijspeert. "From cineradiography to biorobots: an approach for designing robots to emulate and study animal locomotion." Journal of The Royal Society Interface 13, no. 119 (June 2016): 20151089. http://dx.doi.org/10.1098/rsif.2015.1089.

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Robots are increasingly used as scientific tools to investigate animal locomotion. However, designing a robot that properly emulates the kinematic and dynamic properties of an animal is difficult because of the complexity of musculoskeletal systems and the limitations of current robotics technology. Here, we propose a design process that combines high-speed cineradiography, optimization, dynamic scaling, three-dimensional printing, high-end servomotors and a tailored dry-suit to construct Pleurobot: a salamander-like robot that closely mimics its biological counterpart, Pleurodeles waltl . Our previous robots helped us test and confirm hypotheses on the interaction between the locomotor neuronal networks of the limbs and the spine to generate basic swimming and walking gaits. With Pleurobot, we demonstrate a design process that will enable studies of richer motor skills in salamanders. In particular, we are interested in how these richer motor skills can be obtained by extending our spinal cord models with the addition of more descending pathways and more detailed limb central pattern generator networks. Pleurobot is a dynamically scaled amphibious salamander robot with a large number of actuated degrees of freedom (DOFs: 27 in total). Because of our design process, the robot can capture most of the animal's DOFs and range of motion, especially at the limbs. We demonstrate the robot's abilities by imposing raw kinematic data, extracted from X-ray videos, to the robot's joints for basic locomotor behaviours in water and on land. The robot closely matches the behaviour of the animal in terms of relative forward speeds and lateral displacements. Ground reaction forces during walking also resemble those of the animal. Based on our results, we anticipate that future studies on richer motor skills in salamanders will highly benefit from Pleurobot's design.

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Waqar, Muhammad, Imran, Muhammad Atif Zaman, Muhammad Muzammal, and Jungsuk Kim. "Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning." Applied Sciences 12, no. 13 (July 4, 2022): 6772. http://dx.doi.org/10.3390/app12136772.

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Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing.

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Pandey, Abhishek, and Soumya Banerjee. "Test Suite Optimization Using Firefly and Genetic Algorithm." International Journal of Software Science and Computational Intelligence 11, no. 1 (January 2019): 31–46. http://dx.doi.org/10.4018/ijssci.2019010103.

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Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

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Singh, Shilpi, and Raj Shree. "A Multi Criteria based Test Suite Optimization Framework." International Journal of Software Engineering and Its Applications 11, no. 1 (January 31, 2017): 77–86. http://dx.doi.org/10.14257/ijseia.2017.11.1.08.

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Wang, Handing, Yaochu Jin, and John Doherty. "A Generic Test Suite for Evolutionary Multifidelity Optimization." IEEE Transactions on Evolutionary Computation 22, no. 6 (December 2018): 836–50. http://dx.doi.org/10.1109/tevc.2017.2758360.

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Anwar, Zeeshan, and Ali Ahsan. "Exploration and analysis of regression test suite optimization." ACM SIGSOFT Software Engineering Notes 39, no. 1 (February 11, 2014): 1–5. http://dx.doi.org/10.1145/2557833.2557841.

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Jeyaprakash, Srividhya, and K. Alagarsamy. "A Distinctive Genetic Approach for Test-Suite Optimization." Procedia Computer Science 62 (2015): 427–34. http://dx.doi.org/10.1016/j.procs.2015.08.437.

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Pandey, Abhishek, and Soumya Banerjee. "Test Suite Optimization Using Chaotic Firefly Algorithm in Software Testing." International Journal of Applied Metaheuristic Computing 8, no. 4 (October 2017): 41–57. http://dx.doi.org/10.4018/ijamc.2017100103.

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Software testing is time consuming and a costly activity. Effective generation of test cases is necessary in order to perform rigorous testing. There exist various techniques for effective test case generation. These techniques are based on various test adequacy criteria such as statement coverage, branch coverage etc. Automatic generation of test data has been the primary focus of software testing research in recent past. In this paper a novel approach based on chaotic behavior of firefly algorithm is proposed for test suite optimization. Test suite optimization problem is modeled in the framework of firefly algorithm. An Algorithm for test optimization based on firefly algorithm is also proposed. Experiments are performed on some benchmark Program and simulation results are compared for ABC algorithm, ACO algorithm, GA with Chaotic firefly algorithm. Major research findings are that chaotic firefly algorithm outperforms other bio inspired algorithm such as artificial bee colony, Ant colony optimization and Genetic Algorithm in terms of Branch coverage in software testing.

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Wang, Haochen, Yafeng Ju, Kai Zhang, Chengcheng Liu, Hongwei Yin, Zhongzheng Wang, Zhigang Yu, Ji Qi, Yanzhong Wang, and Wenzheng Zhou. "Saturation and Pressure Prediction for Multi-Layer Irregular Reservoirs with Variable Well Patterns." Energies 16, no. 6 (March 14, 2023): 2714. http://dx.doi.org/10.3390/en16062714.

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The well pattern and boundary shape of reservoirs determine the distribution of the remaining oil distribution to a large extent, especially for small-scale reservoir blocks. However, it is difficult to replicate experiences from other reservoirs directly to predict the remaining oil distribution because of the variety of irregular boundary shapes and corresponding well patterns. Meanwhile, the regular well pattern can hardly suit irregular boundary shapes. In this paper, we propose a well placement method for undeveloped irregular reservoirs and a multi-step prediction framework to predict both oil saturation and pressure fields for any reservoir shape and well pattern. To boost the physical information of input characteristics, a feature amplification approach based on physical formulae is initially presented. Then, 3D convolution technology is employed for the first time in 3D reservoir prediction to increase the spatial information in the vertical direction of the reservoir in the input. Moreover, to complete the two-field prediction, the concept of multi-task learning is adopted for the first time, improving the rationality of the forecast. Through the loss-based ablation test, we found that the operation we adopt will increase the accuracy of prediction to some extent. By testing on both manually designed and real irregular-shape reservoirs, our method is proven to be an accurate and fast oil saturation prediction method with its prediction loss less than 0.01 and calculation time less than 10 s in the future one year.

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Ramasundaram, T., and V. Sangeetha . "Bio-Inspired Gradient Genetic Optimization for Test Suite Generation." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 99–107. http://dx.doi.org/10.26438/ijcse/v7i1.99107.

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HIBINO, Yusuke, Hirofumi IKEO, and Nagisa ISHIURA. "CF3: Test Suite for Arithmetic Optimization of C Compilers." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E100.A, no. 7 (2017): 1511–12. http://dx.doi.org/10.1587/transfun.e100.a.1511.

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Khari, Manju. "Empirical Evaluation of Automated Test Suite Generation and Optimization." Arabian Journal for Science and Engineering 45, no. 4 (July 1, 2019): 2407–23. http://dx.doi.org/10.1007/s13369-019-03996-3.

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Chen, Tsong Yueh, and Man Fai Lau. "Dividing strategies for the optimization of a test suite." Information Processing Letters 60, no. 3 (November 1996): 135–41. http://dx.doi.org/10.1016/s0020-0190(96)00135-4.

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Jiang, Shouyong, Marcus Kaiser, Shengxiang Yang, Stefanos Kollias, and Natalio Krasnogor. "A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization." IEEE Transactions on Cybernetics 50, no. 6 (June 2020): 2814–26. http://dx.doi.org/10.1109/tcyb.2019.2896021.

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Cheng, Ran, Miqing Li, Ye Tian, Xingyi Zhang, Shengxiang Yang, Yaochu Jin, and Xin Yao. "A benchmark test suite for evolutionary many-objective optimization." Complex & Intelligent Systems 3, no. 1 (March 2017): 67–81. http://dx.doi.org/10.1007/s40747-017-0039-7.

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Et.al, Chetan J. Shingadiya. "Genetic Algorithm for Test Suite Optimization: An Experimental Investigation of Different Selection Methods." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 3778–87. http://dx.doi.org/10.17762/turcomat.v12i3.1661.

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Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of software product effective testing should be performed. So to overcome these issues of optimization, we have proposed new approach for test suite optimization using genetic algorithm (GA). Genetic algorithm is evolutionary in nature so it is often used for optimization of problem by researcher. In this paper, our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and then we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. In this paper our main objectives as per the experimental investigation, we show that tournament selection works very fine as compared to other methods with respect fitness selection of test case and test suites, testing time of test case and test suites as well as number of requirements.

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Zheng, Liming, and Shiqi Luo. "Adaptive Differential Evolution Algorithm Based on Fitness Landscape Characteristic." Mathematics 10, no. 9 (May 1, 2022): 1511. http://dx.doi.org/10.3390/math10091511.

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Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape conditions to find the optimal solution, and there is not a single strategy that can be suitable for all fitness landscapes. As a result, developing a strategy to adaptively steer population evolution based on fitness landscape is critical. Motivated by this fact, in this paper, a novel adaptive DE based on fitness landscape (FL-ADE) is proposed, which utilizes the local fitness landscape characteristics in each generation population to (1) adjust the population size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The adaptive mechanism is based on local fitness landscape characteristics of the population and enables to decrease or increase the population size during the search. Due to the adaptive adjustment of population size for different fitness landscapes and evolutionary processes, computational resources can be rationally assigned at different evolutionary stages to satisfy diverse requirements of different fitness landscapes. Besides, the DE/current-to-pcbest mutation strategy, which randomly chooses one of the top p% individuals from the archive cbest of local optimal individuals to be the pcbest, is also an adaptive strategy based on fitness landscape characteristic. Using the individuals that are approximated as local optimums increases the algorithm’s ability to explore complex multimodal functions and avoids stagnation due to the use of individuals with good fitness values. Experiments are conducted on CEC2014 benchmark test suit to demonstrate the performance of the proposed FL-ADE algorithm, and the results show that the proposed FL-ADE algorithm performs better than the other seven highly performing state-of-art DE variants, even the winner of the CEC2014 and CEC2017. In addition, the effectiveness of the adaptive population mechanism and DE/current-to-pcbest mutation strategy based on landscape fitness proposed in this paper are respectively verified.

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Bharathi, M. "Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm." International Journal of Applied Metaheuristic Computing 13, no. 1 (January 2022): 1–23. http://dx.doi.org/10.4018/ijamc.2022010106.

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Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

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Ram, J. Prasanth, Dhanup S. Pillai, Ye-Eun Jang, and Young-Jin Kim. "Reconfigured Photovoltaic Model to Facilitate Maximum Power Point Tracking for Micro and Nano-Grid Systems." Energies 15, no. 23 (November 23, 2022): 8860. http://dx.doi.org/10.3390/en15238860.

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PV systems are a popular energy resource, prevalent worldwide; however, shade faults manifested in PV systems limit its power conversion efficiency. The occurrence of multiple power peaks and their location are highly uncertain in PV systems; this necessitates the use of complex maximum power point tracking algorithms to introduce high voltage oscillations. To address this issue, a new reconfigurable PV array to produce a global maximum power point (GMPP) algorithm close to the Voc regions was introduced. This enables the use of a simple Perturb and Observe (P&O) algorithm to easily track GMPP. For reconfiguration, a simple 5 × 5 PV array is considered, and a new physical relocation procedure based on the position square method is proposed. Performance of the proposed reconfiguration model is tested for four various shade events and its row current evaluations are comprehensively analyzed. Furthermore, evaluations of fill factor, mismatch loss, and power loss are quantitatively compared against Dominance Square and TCT schemes. Since the power enhancement is ensured in a reconfigurable PV array, the fixed reconfiguration is tailored to suit residential PV and microgrid systems. For MPP evaluations, hardware demonstrations are performed with a lab scale prototype model developed with a PV simulator and a DC–DC power electronic interface. The I–V characteristics of conventional and reconfigured models are programmed into the simulator and the use of the hill climbing algorithm is validated. To analyze the voltage and power oscillations with MPP tracking, the PSO algorithm is also tested for two test patterns and its results are comprehensively studied.

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M, Bharathi, and Sangeetha V. "Fault-type coverage based ant colony optimization algorithm for attaining smaller test suite." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (September 1, 2020): 507. http://dx.doi.org/10.11591/ijai.v9.i3.pp507-519.

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<table width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="593"><p>In this paper, we proposed Fault-Type Coverage Based Ant Colony Optimization (FTCBACO) technique for test suite optimization. An algorithm starts with initialization of FTCBACO factors using test cases in test suite. Then, assign separate ant to each test case called vertex. Each ant chooses best vertices to attain food source called objective of the problem by means of updating of pheromone trails and higher probability trails. This procedure is repeated up to the ant reaches food source. In FTCBACO algorithm, minimal number of test cases with less execution time chosen by an ant to cover all faults type (objective) are taken as optimal solution. We measured the performance of FTCBACO against Greedy approach and Additional Greedy Approach in terms of fault type coverage, test suite size and execution time. However, the heuristic Greedy approach and Additional Greedy approach required more execution time and maximum test suite size to provide the best resolution for test suite optimization problem. Statistical investigations are performed to finalize the performance significance of FTCBACO with other approaches that concludes FTCBACO technique enriches the reduction rate of test suite and minimizes execution time of reducing test cases efficiently.</p></td></tr></tbody></table>

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Agrawal, Arun Prakash, Ankur Choudhary, and Parma Nand. "An Efficient Regression Test Suite Optimization Approach Using Hybrid Spider Monkey Optimization Algorithm." International Journal of Swarm Intelligence Research 12, no. 4 (October 2021): 57–80. http://dx.doi.org/10.4018/ijsir.2021100104.

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Regression testing validates the modified software and safeguards against the introduction of new errors during modification. A number of test suite optimization techniques relying on meta-heuristic techniques have been proposed to find the minimal set of test cases to execute for regression purposes. This paper proposes a hybrid spider monkey optimization based regression test suite optimization approach and empirically compares its performance with three other approaches based on bat search, ant colony, and cuckoo search. The authors conducted an empirical study with various subjects retrieved from software artifact infrastructure repository. Fault coverage and execution time of algorithm are used as fitness measures to meet the optimization criteria. Extensive experiments are conducted to evaluate the performance of the proposed approach with other search-based approaches under study using various statistical tests like m-way ANOVA and post hoc tests including odds ratio. Results indicate the superiority of the proposed approach in most of the cases and comparable in others.

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Journal articles on the topic 'TEST SUIT OPTIMIZATION'

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