Hybridized Particle Swarm-Gravitational Search Algorithm for Process Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10249736" target="_blank" >RIV/61989100:27230/22:10249736 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000774282700001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000774282700001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/pr10030616" target="_blank" >10.3390/pr10030616</a>
Alternative languages
Result language
angličtina
Original language name
Hybridized Particle Swarm-Gravitational Search Algorithm for Process Optimization
Original language description
The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20300 - Mechanical engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Processes
ISSN
2227-9717
e-ISSN
2227-9717
Volume of the periodical
10
Issue of the periodical within the volume
3
Country of publishing house
CH - SWITZERLAND
Number of pages
13
Pages from-to
nestrankovano
UT code for WoS article
000774282700001
EID of the result in the Scopus database
2-s2.0-85127499801