Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study

Behmanesh, Reza; Rahimi, Iman; Gandomi, Amir H.

doi:10.1007/s11831-020-09415-3

Cited by 34 publications

(14 citation statements)

References 51 publications

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“…Zahra et al [30] verified the efficiency of NSGA-II to solve complex combinatorial optimisation problems. Behmanesh et al [31] evalu-ated18 types of evolutionary multiobjective algorithms and confirmed that NSGA-III is effective for optimising NPhard combinatorial optimisation problems.…”

Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 92%

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Huang

Wang

Chen

2021

International Journal of Aerospace Engineering

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Modern equipment is designed to operate under deteriorating performance conditions resulting from internal ageing and/or external environmental impacts influencing downstream maintenance. This study focuses on the development of a multistate system (MSS) that considers a human reliability factor associated with maintenance personnel—a condition-based multiobjective MSS preventive maintenance model (MSSPMM). The study assumes that no more than one maintenance activity is performed to achieve the most appropriate preventive maintenance (PM) strategy and easy implementation and to reduce maintenance error due to human reliability. The MSS performance based on mean system unavailability and total maintenance cost is evaluated using a stochastic model approach, and then, the MSSPMM is used for optimisation. A customised version of the nondominated sorting genetic algorithm III is employed to ensure efficient solution of the PM model with human reliability—which is considered a constrained multiobjective combinatorial optimisation problem. The optimised solutions are determined from the nondominated Pareto frontier comprising the diversified PM alternatives. A helicopter power transmission system is used as an example to illustrate the efficacy and applicability of the proposed approach through sensitivity analyses with relevant parameters.

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Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 92%

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Huang

Wang

Chen

2021

International Journal of Aerospace Engineering

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“…Amongst the various AI-based optimization techniques, MHAs have proven their accuracy and higher computation efficiency when compared with the other conventional optimization techniques [7]. Therefore, MHAs have been adopted to get the optimum solutions for several engineering problems such as power systems problems, as presented in [95][96][97][98][99][100][101][102][103][104]. Besides, depending on the no freelunch theory [105], many researchers have utilized various algorithms for accurate and effective investigation of the polarization characteristics of the PEMFC, as reported in [5,7,16,57].…”

Section: Mhas For Pemfc's Model Parameters Identificationmentioning

confidence: 99%

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

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“…In recent years, several nature-inspired metaheuristic algorithms have emerged and are attracted by many researchers due to nature as a source of inspiration. However, the literature indicates that metaheuristic algorithms are commonly used in different areas like combinatorial t-way testing, but under-appreciated term [5]. In fact, not all the metaheuristics are effective in combinatorial t-way testing, even though some proved to be very effective and thus have been adopted for optimization whilst others are not adopted.…”

Section: Introductionmentioning

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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Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study

Cited by 34 publications

References 51 publications

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

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

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