Modifikasi Kombinasi Particle Swarm Optimization dan Genetic Algorithm untuk Permasalahan Fungsi Non-Linier

Muchamad Kurniawan, Nanik Suciati

Abstract

Particle Swarm Optimization (PSO) is the population-based optimization algorithm and the generation of random values. The deficiency of the PSO algorithm is prematurely convergent, meaning it quickly finds solutions to local solutions. PSO tidak mampu untuk mencari ruang solusi lebih luas. PSO can not afford to search for wider solution space. In this study modification of the combination of PSO with Genetic Algortihm (GA) or we call M-PSOGA. The advantage of GA taken is to find a wider solution space. M-PSOGA is evaluated on non-linear function problem. The results obtained by M-PSOGA produce the best solution from its predecessor method, PSO and PSOGA. Better on the results of the solutions obtained and the convergent velocity on global solutions.

Keywords: Particel Swarm Optimization, Genetic Algorithm, Non-Linier Function.

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DOI: https://doi.org/10.31284/j.integer.2017.v2i2.177

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