A novel hybrid optimization method of shuffled frog leaping algorithm and particle swarm optimization

作者：Lin, Meijin; Luo, Fei; Xu, Yuge; Luo, Long

来源：2nd International Conference on Key Engineering Materials and Computer Science, KEMCS 2013, Phuket, 2013-03-03 To 2013-03-04.

DOI：10.4028/www.scientific.net/AMR.717.433

• Benchmark functions
• Complex optimization problems
• Fast searching algorithms
• Global optimum solutions
• Hybrid optimization algorithm
• Hybrid optimization method
• Meta heuristic algorithm
• Shuffled frog leaping algorithm (SFLA)

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

• 出版日期2013
• 单位华南理工大学