Primers are critical for polymerase chain reaction (PCR) and influence the success of PCR experiments. Designing large combinations of forward and reverse primers involves various primer constraints, which poses a computational challenge. Most PCR primer design methods limit setting optimal primer parameters because the available algorithms use a single-objective strategy based on primer constraints. This study proposes a new primer design strategy based on a multiobjective approach, called multiobjective primer design, to address the challenge of primer design according to the optimal parameter settings of users. The proposed multiobjective function simultaneously conducts multicriteria evaluation based on primer constraints. Multiobjective particle swarm optimization (MOPSO) was used to determine optimal primers. Single-objective and multiobjective primer design algorithms were evaluated using 2036 DNA sequences. Optimal parameters were set, and each DNA sequence was run 100 times to calculate the difference between the optimal parameters and primer constraint values. The multiobjective primer design had better performance than the single-objective primer design for 11 primer constraints. Practical gel electrophoresis was conducted to verify the PCR experiments, and the wet-laboratory validation established that MOPSO provides feasible primer design. The novel computational approach can help to further develop primer design algorithms for specific experimental situations.
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