Hybrid flower pollination algorithm strategies for t-way test suite generation

Nasser, Abdullah B.; Zamli, Kamal Z.; Alsewari, AbdulRahman A.; Ahmed, Bestoun S.

doi:10.1371/journal.pone.0195187

Cited by 47 publications

(20 citation statements)

References 48 publications

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“…The dotted line box shows the proposed modification in basic FPA. pollen population size, and maximum number of improvements were set at 0.8, 50, and 600, respectively, as suggested in a related study (Nasser, Zamli, Alsewari, & Ahmed, 2018). Based on the same FPA parameter settings, the testing strategies was also the same as only one operator (i.e.…”

Section: T-way Test Suite Generation Based On the Proposed Strategymentioning

confidence: 99%

Dynamic Probability Selection for Flower Pollination Algorithm Based on Metropolishastings Criteria

Zamli

Din

Nasser

et al. 2020

Journal of Information and Communication Technology

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Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm that adopts its metaphor from the proliferation role of flowers in plants. Having only one parameter control (i.e. the switch probability, pa) to choose from the global search (i.e. exploration) and local search (i.e. exploitation) is the main strength of FPA as compared to other meta-heuristic algorithms. However, FPA still suffers from variability of its performance as there is no one size that fits all values for pa, depending on the characteristics of the optimisation function. This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. Adopting the problem of t-way test suite generation as the case study and with the comparative evaluation with the original FPA, FPA-MH gave promising results owing to its dynamic and adaptive selection of search operators based on the need of the current search.

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Section: T-way Test Suite Generation Based On the Proposed Strategymentioning

confidence: 99%

Dynamic Probability Selection for Flower Pollination Algorithm Based on Metropolishastings Criteria

Zamli

Din

Nasser

et al. 2020

Journal of Information and Communication Technology

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“…Software module clustering has been used in the literature within software reverse engineering to assess the software comprehension, evolution, and maintenance [15][16][17]. In particular, the interest on software module clustering problem has been advocated by recent emergence of the new field called Search based Software Engineering (SBSE) [18][19][20][21]. Much work has been done in the maintenance part to help to identify and group (i.e., cluster) those modules (sometimes called subsystems) with common features.…”

Section: Related Work On Software Module Clusteringmentioning

confidence: 99%

Multi-Start Jaya Algorithm for Software Module Clustering Problem

Zamli¹,

Alsewari²,

Ahmed³

2018

AZJHPC

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Jaya algorithm has gained considerable attention lately due to its simplicity and requiring no control parameters (i.e. parameter free). Despite its potential, Jaya algorithm is inherently designed for single objective problems. Additionally, Jaya is limited by the intense conflict between exploration (i.e. roams the random search space at the global scale) and exploitation (i.e. neighborhood search by exploiting the current good solution). Thus, Jaya requires better control for exploitation and exploration in order to prevent premature convergence and avoid being trapped in local optima. Addressing these issues, this paper proposes a new multi-objective Jaya variant with a multi-start adaptive capability and Cuckoo search like elitism scheme, called MS-Jaya, to enhance its exploitation and exploration allowing good convergence while permitting more diverse solutions. To assess its performances, we adopt MS-Jaya for the software module clustering problem. Experimental results reveal that MS-Jaya exhibits competitive performances against the original Jaya and state-of-the-art parameter free meta-heuristic counterparts consisting of Teaching Learning based Optimization (TLBO), Global Neighborhood Algorithm (GNA), Symbiotic Optimization Search (SOS), and Sine Cosine Algorithm (SCA).

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“…Aggressive diversification may lead to inefficient search and slows down the overall search performance [12,13]. b) The operators or components that used for performing the intensification and diversification such as selection mutation, and crossover in Genetic Algorithm (GA) or local and global pollinations in Flower Pollination Algorithm (FPA) [14].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

et al. 2021

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Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.

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Hybrid flower pollination algorithm strategies for t-way test suite generation

Cited by 47 publications

References 48 publications

Dynamic Probability Selection for Flower Pollination Algorithm Based on Metropolishastings Criteria

Dynamic Probability Selection for Flower Pollination Algorithm Based on Metropolishastings Criteria

Multi-Start Jaya Algorithm for Software Module Clustering Problem

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

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