2022
DOI: 10.25007/ajnu.v11n3a1499
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Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Abstract: Metaheuristic algorithms are a collection of sophisticated techniques that mimic natural phenomena and the rational behavior of socially intelligent living organisms like insects and animals. These techniques are employed in the fields of computer science and engineering to address various optimization problems. In this paper, the vibrating particles system(VPS) which is a recently developed metaheuristic algorithm. Generally, an under-damped single degree of freedom (SDOF) free vib… Show more

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Cited by 3 publications
(6 citation statements)
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“…This is a typical constrained optimization problem. Using metaheuristic algorithms to solve constrained optimization problems is an effective and efficient choice [32]. The commonly used metaheuristic algorithms include the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and so on.…”
Section: Optimization Of Weldments' Installation Position Using the P...mentioning
confidence: 99%
“…This is a typical constrained optimization problem. Using metaheuristic algorithms to solve constrained optimization problems is an effective and efficient choice [32]. The commonly used metaheuristic algorithms include the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and so on.…”
Section: Optimization Of Weldments' Installation Position Using the P...mentioning
confidence: 99%
“…Constrained optimization problems involve finding the best solution to an optimization objective while satisfying a set of constraints. Unlike unconstrained optimization problems where only the objective function needs to be considered, constrained optimization problems require considering the feasibility of solutions within the constraints imposed by the problem (Almufti, 2022b).…”
Section: Constrained Optimization Problemmentioning
confidence: 99%
“…Zhang et al, 2008) Constrained optimization problems are prevalent in various fields, including engineering design, finance, operations research, and machine learning. Examples of constrained optimization problems include structural design optimization, portfolio optimization with investment constraints, scheduling problems with resource limitations, and parameter estimation with inequality constraints (Almufti, 2022c).…”
Section: Constrained Optimization Problemmentioning
confidence: 99%
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