Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators
Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms...
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| Published in: | Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 8; pp. 5358 - 5387 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.08.2021
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced. |
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| AbstractList | Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced. |
| Author | Pho, Kim-Hung Nejatian, Samad Bagherifard, Karamollah Ghasemi, Mohsen Parvin, Hamid |
| Author_xml | – sequence: 1 givenname: Mohsen surname: Ghasemi fullname: Ghasemi, Mohsen organization: Department of Computer Engineering, Yasooj Branch, Islamic Azad University – sequence: 2 givenname: Karamollah surname: Bagherifard fullname: Bagherifard, Karamollah organization: Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University – sequence: 3 givenname: Hamid surname: Parvin fullname: Parvin, Hamid email: parvin@iust.ac.ir organization: Institute of Research and Development, Duy Tan University, Faculty of Information Technology, Duy Tan University, Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University – sequence: 4 givenname: Samad surname: Nejatian fullname: Nejatian, Samad organization: Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Department of Electrical Engineering, Yasooj Branch, Islamic Azad University – sequence: 5 givenname: Kim-Hung surname: Pho fullname: Pho, Kim-Hung organization: Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University |
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| Keywords | Next release problem Fairness quality indicator Whale optimization algorithm Grey wolf optimizer Uncertainty size |
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| SubjectTerms | Artificial Intelligence Computer Science Evolutionary algorithms Indicators Machines Manufacturing Mechanical Engineering Multiple objective analysis Optimization algorithms Pareto optimization Processes Uncertainty |
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| Title | Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators |
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