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Extending instruction set architecture (ISA) of embedded processors is an effective way to enhance performance and energy efficiency. The typical approaches for identifying custom instructions (CIs) limit the maximum number of input and output (I/O) operands to the available register file port. Recently, there are several work that explore CI candidates without imposing a limit on the number of input and output operands. In this paper, we present a new algorithm based on Particle Swarm Optimization (PSO) to identify CIs within a given data flow graph (DFG) and evaluate it for both categories of CI identification approaches (with and without I/O constrains). By novel evolving strategy, we enhance the quality of the results in our partitioning algorithm. Experimental results show that in most cases CI identification with I/O constraints based on PSO finds better or the same CIs in terms of performance compared to genetic algorithm (GA) and ISEGEN  (96% and 90%, respectively). Comparing our proposed algorithm with  and  reveals that ours has a shorter run-time several order of magnitudes for large DFGs and is independent of the number of forbidden nodes. Moreover, we propose a modified version of PSO called Wrapper PSO that is up to 100× and 500× faster than GA and ISEGEN in large DFGs, respectively.