Multi-strategy fusion improved Archimedes optimization algorithm

To solve the problems of the AOA algorithm, such as the weak ability of global exploration and local development, low accuracy, and easily falling into local optimum, a multi- strategy fusion improved Archimedes optimization algorithm (MAOA) is proposed. First, two kinds of chaotic mapping and rever...

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Vydáno v:2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP) s. 1280 - 1284
Hlavní autoři: Zhan, Kaijie, Cai, Maoguo, Hong, Guangjie
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 21.04.2023
Témata:
Archimedes optimization algorithm
Chaos reverse learning
Gaussian mutation
Metaheuristics
Segment weight
Signal processing
Signal processing algorithms
Simulation
Sociology
Stability analysis
Statistics
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Shrnutí:To solve the problems of the AOA algorithm, such as the weak ability of global exploration and local development, low accuracy, and easily falling into local optimum, a multi- strategy fusion improved Archimedes optimization algorithm (MAOA) is proposed. First, two kinds of chaotic mapping and reverse learning are used to initialize the population, so that the population has a good initial solution. Secondly, the transfer operator is improved to make the conversion between global exploration and local development not monotonous and enhance the convergence ability. Finally, the adaptive Gaussian mutation strategy is introduced in the local development stage to jump out of the local optimum as much as possible and improve the convergence accuracy. The simulation experiment compares the MAOA algorithm with the standard AOA algorithm and other meta-heuristic algorithms under eight benchmark test functions. The experimental results show that the MAOA algorithm has good comprehensive performance in terms of solution accuracy and convergence speed.
DOI:10.1109/ICSP58490.2023.10248221