An Improved Jaya Optimization Algorithm with Hybrid Logistic-Sine-Cosine Chaotic Map

Jaya optimization algorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but...

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Veröffentlicht in:2022 14th International Conference on Advanced Computational Intelligence (ICACI) S. 176 - 181
Hauptverfasser: Lei, Weidong, Zhang, Zhanbo, Zhu, Jiawei, Lin, Yishuai, Hou, Jing, Sun, Ying
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 15.07.2022
Schlagworte:
Benchmark testing
Complexity theory
Computational intelligence
continuous optimization
hybrid chaotic maps
improved Jaya optimization algorithm
intelligent optimization algorithm
Optimization
Optimization methods
Sociology
Statistics
swarm intelligence
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Zusammenfassung:Jaya optimization algorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but there still has much room to improve its performance, especially for solving high-dimensional and non-convex problems. Hence, this paper proposes an improved Jaya optimization algorithm with a novel hybrid logistic-sine-cosine chaotic map, which is named IJaya for short. The hybrid logisticsine-cosine chaotic map is applied to balance the exploration and the exploitation processes of Jaya optimization algorithm. Seven benchmark testing functions with different scale settings are used to evaluate the performance of our improved algorithm. Computational results indicate that our improved Jaya optimization algorithm outperforms greatly its original version on most testing functions with high-dimensions.
DOI:10.1109/ICACI55529.2022.9837758