Parallel multi-objective artificial bee colony algorithm for software requirement optimization

Alrezaamiri, Hamidreza; Ebrahimnejad, Ali; Motameni, Homayun

doi:10.1007/s00766-020-00328-y

Cited by 28 publications

(21 citation statements)

References 34 publications

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“…This approach has been applied, successfully and prolifically, to different aspects of requirements selection such as requirements prioritization, requirements selection, release planning, Next Release Problem and requirement triage [36]. The applied solving strategy varies from algae algorithms [35], whale and grey wolf optimization [23], bee colony approaches [4], anytime algorithms [17], clustering [40,20].…”

Section: Motivationmentioning

confidence: 99%

An estimation of distribution algorithm based on interactions between requirements to solve the bi-objective Next Release Problem

Sagrado

Ibañez

Cano

2023

Journal of Systems and Software

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Section: Motivationmentioning

confidence: 99%

An estimation of distribution algorithm based on interactions between requirements to solve the bi-objective Next Release Problem

Sagrado

Ibañez

Cano

2023

Journal of Systems and Software

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“…The best solution was then discovered instead of Pareto optimal solutions. Alrezaamiri et al [8], used the master-slave model to present the first method which used parallelism to solve MNRP. On the master and slave base, numerous MABC algorithms were run simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…So, the NRP is treated as a multi‐objective optimisation problem (MOP) due to the conflict between objectives, signalling that the traditional optimisation techniques cannot solve NRP effectively. Therefore, multi‐objective optimisation algorithms is needed [8], which is the proposal to optimise many objective functions at once. For such problems, solutions group recognised as the non‐dominated solutions or Pareto optimal solutions' set has been emerged [6].…”

Section: Introductionmentioning

confidence: 99%

Software requirement selection using a combined multi‐objective optimisation technique

et al. 2022

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The optimal requirements selection set aims primarily at careful search for the best requirements set of the next release of software during development process. This procedure is widely defined as the next release problem (NRP), which is also classified as NP-hard dilemma. Several techniques, in literature, have been proposed to tackle NRP. However, in real examples, the earlier studies still immature as NRP still suffers interactions and restrictions that makes the problem more complicated. Although few interesting works have been presented, yet NRP, based on our study, could be further investigated and effectively tackled. In this research, therefore, NRP is devised as a multiobjective optimisation problem. Two clashing objectives (satisfaction and cost) and two constraints (interactions forms) are formulated. To tackle NRP effectively, a new hybrid genetic and artificial bee colony algorithm (HGABC) is introduced. HGABC combines features of genetic and artificial bee colony algorithms. Experimental study, using case studies and three criteria, have been conducted to show HGABC's power of generating non-dominated effective Pareto solutions versus the state-of-the-art algorithms. Results indicate that HGABC does not just outperform its rivals, yet also gives better Pareto solutions in terms of diversity and quality for almost all the instances of this problem.

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“…PMOABC is an algorithm based on the master-slave model in the shared memory architecture. The authors could reach better solutions with this algorithm in comparison to the ABC algorithm [24]. Grisales-Noreña et al introduced a parallel particle swarm optimization algorithm based on the master-slave model for an energy management system.…”

Section: B) Delay Of Networkmentioning

confidence: 99%

A New Model of Parallel Particle Swarm Optimization Algorithm for Solving Numerical Problems

Pirozmand

Alrezaamiri

Ebrahimnejad

et al. 2021

MJCS

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Evolutionary algorithms are suitable methods for solving complex problems. Many changes have thus been made on their original structures in order to obtain more desirable solutions. Parallelization is a suitable technique to decrease the runtime of the algorithm, and therefore, to obtain solutions with higher quality. In this paper, a new algorithm is proposed with two approaches, which is based on a parallelization technique with shared memory architecture. In the proposed algorithm, the search space is firstly decomposed into multiple equal and independent subspaces. Then, a subtask is performed on each subspace simultaneously in a parallel manner which leads to providing more qualified solutions. Splitting the search space into smaller subspaces causes the algorithm to find optimal solutions in each region in an easier way. The algorithm RAPSO is improved with applying a new acceleration coefficient which has been named IRAPSO. In the proposed algorithm, the IRAPSO is used as the subtask. For the sake of testing the proposed algorithm, fourteen well-known benchmarks of numerical optimizing problems are inspected. Then, the proposed algorithm is compared with algorithms PPBO and PSOPSO that were both based on the island model. The results of the proposed algorithm are much better than those of the other two algorithms.

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Parallel multi-objective artificial bee colony algorithm for software requirement optimization

Cited by 28 publications

References 34 publications

An estimation of distribution algorithm based on interactions between requirements to solve the bi-objective Next Release Problem

An estimation of distribution algorithm based on interactions between requirements to solve the bi-objective Next Release Problem

Software requirement selection using a combined multi‐objective optimisation technique

A New Model of Parallel Particle Swarm Optimization Algorithm for Solving Numerical Problems

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