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Optimizing the BIST configuration based on the characteristics of the design under test is a complicated and challenging work for test engineers. Since this problem has multiple optimization factors, trapping in local optimums is very plausible. Therefore, regular computing algorithms cannot efficiently resolve this problem and utilization of some algorithms is required. In this work, by applying genetic algorithm (GA) and particle swarm optimization (PSO) - which are two well-known bio-inspired computing algorithms -, reconfiguring an optimum parametric BIST is exercised. These methods are applied to configure a parametric BIST for some ISCAS benchmarks, and the efficiency of the resulted configuration is evaluated by means of Verilog HDL procedural language interface (PLI).Using HDL environment along with bio-inspired algorithms, significant advantages over previous works are obtained.