Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

Ghasemi, Mohsen; Bagherifard, Karamollah; Parvïn, Hamïd; Nejatian, Samad; Pho, Kim-Hung

doi:10.1007/s10489-020-02018-2

Cited by 20 publications

(13 citation statements)

References 79 publications

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“…Tables [13][14][15][16][17][18] are showing the results of the t-test to compare HGABC's quality indicators versus rival algorithms as the following:…”

Section: Discussionmentioning

confidence: 99%

“…MNRP was presented and devised as a MOP [6]. MONRP was tackled through multi‐objective NSGA‐II, GA, SPEA2, and PESA [7, 11, 12], WOA, GWO, SPEA2 and NSGA‐II, [13, 14]. The NRP [5, 12, 15] and MNRP [7, 12, 16] have been widely tackled using genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Given the negative implications of swelling the number of objective functions on the quality of the solutions, the uncertainty and fairness are considered quality indicators instead of objective functions. Mohsen et al., sought to tackle the MNRP using five multi‐objective evolutionary algorithms including grey wolf and whale optimisation [13].…”

Section: Related Workmentioning

confidence: 99%

“…PESA [7,11,12], WOA, GWO, SPEA2 and NSGA-II, [13,14]. The NRP [5,12,15] and MNRP [7,12,16] have been widely tackled using genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

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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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“…Tables [13][14][15][16][17][18] are showing the results of the t-test to compare HGABC's quality indicators versus rival algorithms as the following:…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…PESA [7,11,12], WOA, GWO, SPEA2 and NSGA-II, [13,14]. The NRP [5,12,15] and MNRP [7,12,16] have been widely tackled using genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

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Software requirement selection using a combined multi‐objective optimisation technique

et al. 2022

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“…Their study can help DMs provide the clients with fair allocation of requirements based on various fair metrics. During the decision-making stage, Ghasemi et al [153] suggested to take different quality indicators like fairness in requirement assignment to compare the obtained Pareto non-dominated solutions of NRP [152], which plays an important role in requirement analysis or decision making.…”

Section: Fairness In Multi-objective Optimizationmentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

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Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and datadriven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

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Application of improved hybrid whale optimization algorithm to optimization problems

Uzer

Inan

2023

Neural Comput & Applic

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Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

Cited by 20 publications

References 79 publications

Software requirement selection using a combined multi‐objective optimisation technique

Software requirement selection using a combined multi‐objective optimisation technique

Towards Fairness-Aware Multi-Objective Optimization

Application of improved hybrid whale optimization algorithm to optimization problems

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