Solving Frequency Modulation Sound Parameter Identification Problem using a Modified Shuffled Particle Swarm Optimization
Morteza Alinia Ahandani
1
(
Department of Electrical Engineering, Langarud Branch, Islamic Azad University, Langarud, Iran
)
Hosein Alavi-Rad
2
(
Department of Electrical Engineering, Langarud Branch, Islamic Azad University, Langarud, Iran
)
Mohammad Khoshhal
3
(
Department of Electrical Engineering, Langarud Branch, Islamic Azad University, Langarud, Iran
)
Keywords: Shuffled particle swarm optimization, Partitioning, Frequency modulation, Parameter identification, Inertia weigh factor,
Abstract :
Frequency-Modulated (FM) sound wave synthesis has an important role in modern music systems to optimize the parameter of an FM synthesizer. This paper proposes a modified version of shuffled particle swarm optimization (SPSO) for solving two versions of the frequency modulation sound parameter identification (FMSPI) problem. i.e nested-modulator FM and double-modulator FM. In the SPSO a population is divided into several parallel groups and then each group is improved in an evolutionary process using a particle swarm optimization (PSO). This research on one side, focuses on partitioning stage of SPSO. Three different partitioning strategies are employed and compared together. On the other side, a new methodology to update inertia weigh factor of SPSO is presented and evaluated. A geometric partitioning provides a good exploration of search space and independent local search of each group leads to a deep exploitation. Also new proposed idea to update inertia weight factor prevents for stagnation and a premature convergence of algorithm. The obtained results demonstrate that the geometric partitioning and new method to update the inertia weight factor lead to a considerably better performance of algorithm than other compared strategies so that the SPSO using these strategies is very effective and robust. Also a comparison of the SPSO against other evolutionary algorithms reported in the literature confirms a better or at least comparable performance of our proposed algorithm.
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