Differential evolution and particle swarm optimization against COVID-19

Abstract

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.

Global Optimization Theories and Techniques for COVID-19

“…Particle Swarm Optimization (PSO) is a computational optimization technique inspired by the social behavior of birds and fish [25]. While PSO itself is not a direct solution for addressing COVID-19, it can be applied in several ways to assist in managing the pandemic more effectively [26]: At each iteration 𝑡𝑡 , the 𝑖𝑖 th particle is represented by a vector 𝑥𝑥 𝑖𝑖 𝑡𝑡 in multidimensional space to characterize its position. The vector 𝑣𝑣 𝑖𝑖 𝑡𝑡 is used to characterize its velocity.…”

Electromechanical Integration Construction of Automobile Inspection Equipment Based on Particle Swarm Optimization

“…Mechatronics refers to integrating multiple complex machines into a complete system through a certain method, so that they can be automatically measured at different locations. It includes sensors, controllers and other equipment [11][12]. These devices can provide corresponding information and control commands to the observation objects to meet the needs of users, and can also realize the multi-level switch detection function (that is, a full channel is formed between the detector and the microcontroller) or multi-channel output mode switching, and achieve the optimal configuration of performance parameters and the realization of the optimal working state through rational planning of the internal body.…”

Novel Air2water Model Variant for Lake Surface Temperature Modeling With Detailed Analysis of Calibration Methods

“…Although the literature is full of metaheuristics [28]- [32], according to No Free Lunch theorems, the performance of all possible non-revisiting heuristics on all possible problems > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < would be equal [33]. Nonetheless, the performances of metaheuristics do differ for the specific problems, and may highly affect applications to such practical problems like the Internet of Things [34], COVID-19 pandemic [35], robotics [36], photovoltaic systems [37], and various hydrological [38], planetary [39], or remote sensing [40] problems. The question how to choose appropriate metaheuristic for the particular application is old and ubiquitous [41]- [45].…”