An Improved Harmony Search Algorithm and Its Application in Function Optimization
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초록
Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by themusic improvisation process and can solve different optimization problems. In order to further improve theperformance of the algorithm, this paper proposes an improved harmony search algorithm. Key parametersincluding harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) areoptimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improvedmethod to generate a new crossover solutions rather than the traditional mechanism of improvisation. Fourcomplex function optimization and pressure vessel optimization problems were simulated using theoptimization algorithm of standard harmony search algorithm, improved harmony search algorithm andexploratory harmony search algorithm. The simulation results show that the algorithm improves the ability tofind global search and evolutionary speed. Optimization effect simulation results are satisfactory.
Abstract
Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by themusic improvisation process and can solve different optimization problems. In order to further improve theperformance of the algorithm, this paper proposes an improved harmony search algorithm. Key parametersincluding harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) areoptimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improvedmethod to generate a new crossover solutions rather than the traditional mechanism of improvisation. Fourcomplex function optimization and pressure vessel optimization problems were simulated using theoptimization algorithm of standard harmony search algorithm, improved harmony search algorithm andexploratory harmony search algorithm. The simulation results show that the algorithm improves the ability tofind global search and evolutionary speed. Optimization effect simulation results are satisfactory.
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