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16 December 2022 Improved adaptive genetic simulated annealing algorithm
Junfei Zhang, Limin Tao
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Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 1250065 (2022) https://doi.org/10.1117/12.2662641
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China
Abstract
This paper aims to use an improved version of the real coding genetic algorithm to achieve a globally optimal solution to complex problems, such as the Traveling Salesman Problem (TSP). The Genetic Algorithm (GA) is prone to fall into local optimal solutions, and the Simulated Annealing algorithm (SA) converges slowly. In this paper, an optimization algorithm based on an improved Adaptive Genetic Simulated Annealing Algorithm (AGSAA) is proposed. Then an adaptive crossover and mutation probability is improved, which can effectively avoid the algorithm from falling into local optimum. Finally, a simulated annealing operator is added according to the evolutionary process of the algorithm, and an adaptive Metropolis criterion and a minimum temperature are improved to make the algorithm more adaptive. The experimental results on the TSP example show that the proposed AGSAA can obtain better optimization results compared with the results of other optimization algorithms.
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Junfei Zhang and Limin Tao "Improved adaptive genetic simulated annealing algorithm", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 1250065 (16 December 2022); https://doi.org/10.1117/12.2662641
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KEYWORDS
Algorithms
Genetic algorithms
Optimization (mathematics)
Computer programming
Annealing
Binary data
Computer simulations
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