Abstract
The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/s.
Highlights
Particle swarm optimization (PSO) and differential evolution (DE) are two stochastic, population-based optimization EAs in evolutionary algorithms.PSO was developed by Kennedy and Eberhart and was originally intended to simulate social behaviour
The PSO algorithm has attracted a lot of attention in the last decade, it has a premature convergence issue, which is common in complicated optimization problems
This study develops PSO, DE, and DEPSO models for the design optimization of a
Summary
Particle swarm optimization (PSO) and differential evolution (DE) are two stochastic, population-based optimization EAs in evolutionary algorithms.PSO was developed by Kennedy and Eberhart and was originally intended to simulate social behaviour. Particle swarm optimization (PSO) and differential evolution (DE) are two stochastic, population-based optimization EAs in evolutionary algorithms. Every solution in PSO is a “bird” in the search space [1]. When the technique is implemented, it is referred to as a particle. All of the particles have fitness values that the fitness function evaluates in order to optimize them, as well as velocities that control their flight. The particles navigate through the problem space by following the current best particles. The PSO algorithm has attracted a lot of attention in the last decade, it has a premature convergence issue, which is common in complicated optimization problems. To improve PSO’s search performance, certain strategies for adjusting parameters such as inertia weights and acceleration coefficients have been developed
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