Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

Halim, Abdul; Ismail, Idris; Das, Swagatam

doi:10.1007/s10462-020-09906-6

Cited by 177 publications

(60 citation statements)

References 229 publications

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“…In future work, it would be prudent to improve and develop MOBO using vector-valued GPs with high noise observations and the weight adaptation method to accommodate complex PF shapes. Regarding the performance metrics in cases of complex PF shapes, we think that in addition to Loss and IGD metrics, various metrics [12] need to more precisely quantify the performance of algorithms [9]. In addition, real experiments would be desirable to confirm the effectiveness of combining random scalarizations and vector-valued GPs in materials discovery.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Multi-objective Bayesian Optimization Using Random Scalarizations for Correlated Observations

Ohno

2021

SN COMPUT. SCI.

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Bayesian optimization (BO) has been used for a wide range of applications. However, multi-objectives (e.g., time and cost of moving people and/or products) are often found in practical situations, and thus, multi-objective optimization is often required. In addition, correlated observations are found in various applications, e.g., materials discovery. To deal with such situations effectively, random scalarizations and vector-valued Gaussian processes (GPs) are adopted. For multi-objective BO (MOBO) using random scalarizations (linear and Tchebychev schemes) and vector-valued GPs, regret bounds are analyzed. In terms of Tchebychev scalarizations, these are sub-linear in the number of rounds. Bayes risk decomposition of the GP upper confidence bound (UCB) is utilized for theoretical analysis. In addition, a weight distribution for scalarizing objective functions is estimated by nonstationary Thompson sampling where the Dirichlet distribution is a prior, thereby achieving automatic weight adaptations according to the progress of optimization. Experimental results generated from 10 benchmark functions and one real application (a hydrogen storage material database) testify to the effectiveness of using vector-valued GPs compared to real-valued GPs and the weight adaptations. Moreover, under very noisy observations in the experimental settings, real-valued GPs are found to work better than vector-valued GPs in the context of MOBO convergence. This is because of random fluctuation in posterior variance caused by noisy observations in the UCB.

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Section: Discussionmentioning

confidence: 99%

Analysis of Multi-objective Bayesian Optimization Using Random Scalarizations for Correlated Observations

Ohno

2021

SN COMPUT. SCI.

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“…Choosing the better method among the competitors is very important for practical users, even though various approaches to the problem of comparison between metaheuristics are still debated in the literature (Garcia and Herrera 2008 ; Crepinsek et al 2016 ; Hussain et al 2019 ; Halim et al 2021 ). In the majority of papers in which DE or PSO are used to solve COVID-19 related problems, only one variant of a single optimization method is used.…”

Section: Methodological Aspects Of Differential Evolution and Particle Swarm Optimization Applicationsmentioning

confidence: 99%

Differential evolution and particle swarm optimization against COVID-19

Piotrowski

Piotrowska

2021

Artif Intell Rev

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COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

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“…The total losses are the sum of the Joule losses Pj, the aerodynamic losses Paero and the iron losses Pf. The output power Pout can be expressed as in (11) and the efficiency at rated operating η is expressed as in (12).…”

Section: B Electric Submodelmentioning

confidence: 99%

“…Thus, a fast and precise optimal sizing design can be reached. In addition, to ensure high-fidelity results, a performant metaheuristic optimization algorithm [11] is chosen to calculate the optimal parameters of the PMSG.…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing design of a 1.5 MW permanent magnets synchronous generator for an onshore wind conversion system

Gallas¹,

Ballois²,

Aloui³

et al. 2024

RE&PQJ

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This paper proposes a fast and accurate optimal sizing design of 1.5 MW Permanent Magnets Synchronous Generator (PMSG) for a grid-connected wind application. A design strategy inspired from the output space mapping technique is adopted. A fast analytical model is used and detailed to determine the parameters and the performances of the PMSG. Then, the results are validated by a precise finite element model and adjusted iteratively until coherence between the two models is obtained. A multi-objective particle swarm optimization algorithm is deployed with aim of reducing the total losses and weight of the generator. The algorithm's parameters and results are given and analyzed. Three optimal machines are chosen and tested using a 2D-finite element model. The main design parameters of the optimal generators are given and discussed. Good efficiency and optimal designs are obtained for the sized machines thanks to the adopted design strategy.

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Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

Cited by 177 publications

References 229 publications

Analysis of Multi-objective Bayesian Optimization Using Random Scalarizations for Correlated Observations

Analysis of Multi-objective Bayesian Optimization Using Random Scalarizations for Correlated Observations

Differential evolution and particle swarm optimization against COVID-19

Optimal sizing design of a 1.5 MW permanent magnets synchronous generator for an onshore wind conversion system

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