Improvising the effectiveness of test suites using differential evolution technique

Shilpi,; Karambir,

doi:10.1109/icrito.2016.7784924

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…Here, f 8 − f 20 are multimodal Table 1. Both parameters, scale factor F and crossover probability CR of Classical DE are initialised to 0.5 for all algorithms [22,23]. The parameter l is also initialised as 0.1 according to our experiments for the parameter l, which will be discussed as follows.…”

Section: Benchmark Functionsmentioning

confidence: 99%

“…The strategies parameters are indicated as follows in this paper: Number of independent runs: RUN = 30. Population size: SIZE = 50. Benchmark dimension: D = 30. Maximal function evaluation number: MAXFUNNUM = 100,000. Both parameters, scale factor F and crossover probability CR of Classical DE are initialised to 0.5 for all algorithms [22, 23]. The parameter

λ

is also initialised as 0.1 according to our experiments for the parameter

λ

, which will be discussed as follows.…”

Section: Performance Comparison and Analysismentioning

confidence: 99%

“…Michael et al [21] illustrate how the simple constrained multi-objective optimisation algorithm, Generalised DE 3, can assist the practical sizing of mechatronic components used in digital displacement fluid power machinery. Shilpi and Karambir [22] implemented an optimising technique called DE to improve the effectiveness of test cases using the average percentage of fault detection metric. Du et al [23] proposed an event-triggered impulsive control scheme to improve the performance of DE.…”

Section: Introductionmentioning

confidence: 99%

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Second‐order DE algorithm

Zhao

Liu

et al. 2017

CAAI Transactions on Intelligence Technology

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Differential evolution (DE) is a robust, efficient and simple evolutionary algorithm for various optimisation and engineering problems. It has several outstanding features such as low time complexity, ease to use and robust steadiness. So it is becoming more and more popular and is widely used in more and more applications. However, many questions are deserving to consider the critical balance between global exploration and neighbourhood exploitation. The difference vector of the mutation operator for the direction and neighbour information has not been fully exploited. Therefore, a second-order difference vectors based DE, SODE, is proposed, which can efficiently utilise different direction information from the second-order difference vector. The optimal second-order difference mechanisms are proposed for DE/rand/1 and DE/best/1 to utilise the direction and neighbour information from difference vector. Then, it will guide the individuals toward the possible more encouraging areas. Extensive experiments and comprehensive comparisons show that the second-order differenced mechanism in SODE is much better than the classical first-order difference mechanisms based mutation strategy-' DE/rand/1' and 'DE/best/1' as far as the converging and steady performance.

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Section: Benchmark Functionsmentioning

confidence: 99%