Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm

Alrezaamiri, Hamidreza; Ebrahimnejad, Ali; Motameni, Homayun

doi:10.1007/s00500-018-3553-7

Cited by 28 publications

(15 citation statements)

References 30 publications

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“…Due to the complexity of the problem model, we adopted a relatively new intelligent optimization algorithm-chemical reaction optimization algorithm (CRO) [13][14][15] to solve it. This algorithm is a swarm intelligence algorithm based on the collision and energy conversion of molecules in the process of chemical reaction.…”

Section: Optimization Model Of Mobile Sensor Networkmentioning

confidence: 99%

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Gao

Lyu

et al. 2021

Wireless Communications and Mobile Computing

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Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision-making, and rapid rescuing work in emergencies.

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Section: Optimization Model Of Mobile Sensor Networkmentioning

confidence: 99%

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Gao

Lyu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…When the termination criterion is reached, the algorithm is terminated, like when no more reactions may occur (inert solution). ACROA has addresses some optimization that include the NP hard problem [272], clustering problem [273], and so on. ACROA is not devoted in t-way testing strategy problem.…”

Section: Physic-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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“…As a matter of fact, parallel algorithm runs can produce better results over the sequential runs. Then, utilising the technique of artificial chemical reaction optimisation, the MNRP was tackled [28]. Moreover, a fuzzy inference system‐based method was built to decide the fitness of every requirement for the next‐release development.…”

Section: Related Workmentioning

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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Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm

Cited by 28 publications

References 30 publications

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

[Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Software requirement selection using a combined multi‐objective optimisation technique

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