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In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize parameters in surface grinding processes where multiple conflicting objectives are present. The relationships between surface grinding process parameters and the performance measures of interest are obtained by using experimental data and particle swarm optimization intelligent neural network systems (PSOINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex surface grinding operations optimization problems. In this paper the key grinding process models and relationships that were discovered by previous research efforts have been unified in the form of a particle swarm optimization intelligent neural network systems.

In the original publication, the fourth author name was incorrectly published. The correct name is Deepak Rajendra Unune.