Dynamic Solution Probability Acceptance Within the Flower Pollination Algorithm for Combinatorial t-Way Test Suite Generation

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

doi:10.1007/978-981-13-6031-2_4

Cited by 4 publications

(1 citation statement)

References 19 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computational comparison between the firefly meta-heuristic algorithm, genetic algorithm, and ant colony optimization revealed that the firefly algorithm has a shorter optimization time. Other models developed for solving combinatorial software testing using meta-heuristic searching include the memetic algorithm (MA) in [19], the particle swarm optimization (PSO) in [20], the artificial bee colony (ABC) and corresponding developed variants in [13], [21]- [26], the harmony search algorithm (HSA) in [27], the bat algorithm (BA) in [12], [28]- [30], and the flower pollination algorithm (FPA) in [31] and [32]. All these models are considered pure models because they utilize a common pure meta-heuristic approach.…”

Section: A Csst Testingmentioning

confidence: 99%

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Al-Sammarraie

Jawawi

2020

IEEE Access

View full text Add to dashboard Cite

Combinatorial searching-based software testing (CSST) is a challenging optimization procedure. The achievement of optimal solutions involves a careful formulation of the optimization problem and the selection of an appropriate approach. Meta-heuristic searching procedures have proven to be effective for solving CSST issues. Black hole (BH) optimization is among the more recently developed meta-heuristic searching algorithms. While this approach has been observed to be an effective alternative to particle swarm optimization, its operation is based on only one swarm. To date, no efforts have been made to modify this approach to accommodate multiple swarms. This study proposes a new variant of BH that involves a combination of multiple swarms. The BH optimizer is modified from continuous searching to binary searching and subsequently applied for solving CSST. The evaluation is based on a modified-benchmarking mathematical function and well-known CSST problems. This modified BH method is superior to the original BH and the established particle swarm optimization (PSO) approach. In terms of CSST problems, binary multiple black hole (BMBH) optimizations generate reduction rates between 50% and more than 60% for t = 4 according to the problem. INDEX TERMS Black hole optimization, combinatorial searching-based software testing, meta-heuristic searching, multiple black hole optimization, swarm meta-heuristic.

show abstract