A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints

Campos, Guillermo; Dugardin, Frédéric; Yalaoui, Farouk; Kelly, Russell

doi:10.1016/j.ifacol.2016.07.690

Cited by 92 publications

(25 citation statements)

References 11 publications

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“…In this paper, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted for the multi-objective optimization solving. NSGA-II already gains excellent convergence and diversity performance compared with other variant, and still yields the most concise programing and fast non-dominated sorting for two-objective optimization problem [27], [28], which matches our optimization case, in this paper.…”

Section: A Nsga-ii Based Multi-objective Optimizationsupporting

confidence: 67%

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Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

Ran

Radwan

2020

IEEE Access

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Small cells (SCs) have proven their marvelous capability, especially dealing with high density of user equipment (UE) in the configuration of Internet of Things (IoT). It is widely accepted that the SCs concept is one of the prospective solutions for 5G networks, specifically targeting IoT, to attain the high demand in the future smart city. SC deployment has attracted the attention of multiple research efforts, due to its importance and potential towards enhancing future networking for IoT. However, there is still a lack of works providing SC allocation with comprehensive optimization modeling, which provides essential infrastructural platform to SC functionalities and services (e.g. offloading). The analysis of the number and positions of SCs, towards the simultaneous optimization of both energy efficiency and data rates, is a highly required research direction. SC allocation facilitates the sustainability and reliability, to support exponentially increasing number of users, as well as yielding feasibility of achieving high traffic demand and low latency performance, in addition to longer sustainability based on enhanced energy efficiency. In this paper, a smart small cell allocation scheme is proposed for IoT in smart city. Integer programming multiobjective optimization problem is formulated, and a new algorithm, based on the fusion of Branch-and-Bound (BnB) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is developed. Energy efficiency optimization and data rate maximization are formulated, as mutual objectives to fulfill the key demands of IoT in smart city, based on the estimation of users' behaviors. Simulations are conducted, whose results show that the proposed Smart-SC allocation outperforms the different considered benchmarks, resulting in higher offered data rates, while achieving better energy efficiency.

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Section: A Nsga-ii Based Multi-objective Optimizationsupporting

confidence: 67%

“…With a properly designed maximum iteration, the optimality (i.e. convergence and diversity performance) of NSGA-II is well proven, in previous researches [10], [28].…”

Section: ) Multi-objective Searching Processmentioning

confidence: 96%

Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

Ran

Radwan

2020

IEEE Access

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“…The higher running time of SPEA2 is due to the maintenance of a separate archive population and a higher volume of computations that it needs in order to rank the solutions for selection since it uses an additional factor (i.e., domination strength [116]) to determine the ranks of the solutions. Similarly, previous work showed that NSGA-III was found to be worse than NSGA-II for some problems [49], and generally slower when used with fewer objectives or smaller instances [18]. On the other hand, IBEA, which also uses an additional computation of a quality indicator (i.e., Hypervolume) to rank its solutions, fails to produce good quality solutions for the problem investigated herein.…”

Section: Discussionsupporting

confidence: 54%

Multi-Objective Software Effort Estimation: A Replication Study

Tawosi

Sarro

Petrozziello

et al. 2022

IIEEE Trans. Software Eng.

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Replication studies increase our confidence in previous results when the findings are similar each time, and help mature our knowledge by addressing both internal and external validity aspects. However, these studies are still rare in certain software engineering fields. In this paper, we replicate and extend a previous study, which denotes the current state-of-the-art for multi-objective software effort estimation, namely CoGEE. We investigate the original research questions with an independent implementation and the inclusion of a more robust baseline (LP4EE), carried out by the first author, who was not involved in the original study. Through this replication, we strengthen both the internal and external validity of the original study. We also answer two new research questions investigating the effectiveness of CoGEE by using four additional evolutionary algorithms (i.e., IBEA, MOCell, NSGA-III, SPEA2) and a well-known Java framework for evolutionary computation, namely JMetal (rather than the previously used R software), which allows us to strengthen the external validity of the original study. The results of our replication confirm that: (1) CoGEE outperforms both baseline and state-of-the-art benchmarks statistically significantly (p < 0.001); (2) CoGEE's multi-objective nature makes it able to reach such a good performance; (3) CoGEE's estimation errors lie within claimed industrial human-expert-based thresholds. Moreover, our new results show that the effectiveness of CoGEE is generally not limited to nor dependent on the choice of the multi-objective algorithm. Using CoGEE with either NSGA-II, NSGA-III, or MOCell produces human competitive results in less than a minute. The Java version of CoGEE has decreased the running time by over 99.8% with respect to its R counterpart. We have made publicly available the Java code of CoGEE to ease its adoption, as well as, the data used in this study in order to allow for future replication and extension of our work.

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“…However, when the number of objectives is above three, multiobjective search algorithms, such as NSGA-II [13], do not scale well [9,23]. This is where many-objective search algorithms come into play.…”

Section: Background 21 Search-based Testingmentioning

confidence: 99%

Automatic test suite generation for key-points detection DNNs using many-objective search (experience paper)

Haq

Shin

Briand

et al. 2021

Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

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Automatically detecting the positions of key-points (e.g., facial keypoints or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time-where each individual key-point may be critical in the targeted application-and images can vary a great deal according to many factors.In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining.

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A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints

Cited by 92 publications

References 11 publications

Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

Multi-Objective Software Effort Estimation: A Replication Study

Automatic test suite generation for key-points detection DNNs using many-objective search (experience paper)

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