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 158 publications

(60 citation statements)

References 229 publications

(252 reference statements)

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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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“…Hence, this paper would not impart an exhaustive review of them here. However, reference [14] comprehensively assesses both single-and multi-objective algorithms and proffers a reliable overview of the current metaheuristics state.…”

Section: Optimizermentioning

confidence: 99%

Hypothesis derivation and its verification by a wholly automated many-objective evolutionary optimization system

Chiba

Sawahara

Sumimoto

et al. 2021

Neural Comput & Applic

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This study has constructed a fully automated multidisciplinary and many-objective evolutionary design optimization system independent of computer environments to evaluate objective functions; the research applied it to a geometric design problem of a flyback booster for next-generation space transportation. In optimization involving objective functions to appraise the aero-/structural-dynamic performance with high fidelity, spatial discretization hinders the overall automation. This research has facilitated an efficient optimal design by wholly automating high-fidelity assessments, which designers had to implement manually, and has accomplished optimizations that directly contribute to real-world design problems. Moreover, this study would accumulate design knowledge for space transportation that the market is reviving. The total automated system yielded the embedding of geometric trait lines to ensure the discretization even for large curvature surfaces; the system innovated a robust automatic error-checking mechanism in the system’s preprocess. Consequently, the entirely automatized optimization procured nondominated solution sets for more precise data analyses in a pragmatic execution period. Design informatics, a framework combining optimization and data analysis, functioned usefully in real-world design on flyback-booster geometry by materializing smooth deriving and verifying a design hypothesis; eventually, the research gained a new design principle.

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Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

Cited by 158 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

Hypothesis derivation and its verification by a wholly automated many-objective evolutionary optimization system

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