Optimized Differential Evolution Algorithm for Software Testing

Gou, Xiaodong; Huang, Tingting; Yang, Shunkun; Su, Mengxuan; Zeng, Fuping

doi:10.2991/ijcis.2018.125905642

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Nevertheless, low convergence speed is a problem of a classical DE, which requires high computational efforts. To overcome this restriction, several variations of the DE algorithm have been suggested to improve the performance of DE by modifying the evolution process during the search or integrating some additional processes into the DE to increase the convergence rate, such as local search [ 19 ], external archive [ 20 ], or antiaging mechanisms [ 21 ]. Pant et al [ 22 ] presented an extensive survey of existence of DE.…”

Section: Related Studiesmentioning

confidence: 99%

Selective Strategy Differential Evolution for Stochastic Internal Task Scheduling Problem in Cross-Docking Terminals

Buakum

Wisittipanich

2022

Computational Intelligence and Neuroscience

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This study proposed an algorithm called selective strategy differential evolution (SSDE) to handle the complexity of the stochastic internal task scheduling problem in cross-docking terminals. The aims of this study are to assign workers and transfer equipment to internal operations and sequence those operations under randomness and uncertainty with the purpose to minimise total tardiness. The main feature of SSDE is its ability to adapt itself in order to execute the best search strategy. The proposed algorithm was tested on 16 instances using generated data based on real-case scenarios of a pharmaceutical distribution centre. The results showed the significant performance of SSDE to other existing algorithms in terms of solution quality and computational time. The key success factors of SSDE are the use of various search strategies in a single run and the application of suitable termination conditions.

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Section: Related Studiesmentioning

confidence: 99%

Selective Strategy Differential Evolution for Stochastic Internal Task Scheduling Problem in Cross-Docking Terminals

Buakum

Wisittipanich

2022

Computational Intelligence and Neuroscience

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“…Siemens Suite is widely utilized as the benchmark test software. Siemens Suite has been previously used to evaluate the performance of various techniques [27][28][29] and contains seven programs that were written with the C language ( Table 2). The programs contained in Siemens Suite were developed to study the fault detection of a given test criterion.…”

Section: Software Under Test (Sut)mentioning

confidence: 99%

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020

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Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

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“…The topic of efficient utilization of the differential evolution algorithms in optimization tasks is treated in a row of articles. The articles [1,[4][5][6][7], and [14] deal with possible modifications of the differential evolution algorithms focused on limiting the stagnation of the algorithms and convergence acceleration to the global minimum of the cost function. Simultaneously, some articles concentrate on the division of the optimization process into several local optimization tasks which are subsequently solved using the differential evolution algorithms (see articles [15] and [20]).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Analysis of the Convergence of the Differential Evolution Algorithm

Knobloch¹,

Mlýnek²

2020

NNW

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Differential evolution algorithms represent an efficient framework to tackle complicated optimization problems with many variables and involved constraints. Nevertheless, the classic differential evolution algorithms in general do not ensure the convergence to the global minimum of the cost function. Therefore, the authors of the article designed a modification of these algorithms that guarantees the global convergence in the asymptotic and probabilistic sense. The modification consists in adding a certain ratio of random individuals to each generation formed by the algorithm. The random individuals limit the premature convergence to the local minimum and contribute to more thorough exploration of the search space. This article concentrates specifically on the role of random individuals in the identification of the global minimum of the cost function. Besides, the paper also contains some useful estimates of the probability of finding the global minimum of the corresponding cost function.

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Optimized Differential Evolution Algorithm for Software Testing

Cited by 3 publications

References 21 publications

Selective Strategy Differential Evolution for Stochastic Internal Task Scheduling Problem in Cross-Docking Terminals

Selective Strategy Differential Evolution for Stochastic Internal Task Scheduling Problem in Cross-Docking Terminals

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Probabilistic Analysis of the Convergence of the Differential Evolution Algorithm

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