A hybrid SFLA algorithm based on conjugate gradient method

Abstract

Abstract:

We propose a hybrid shuffled frog leaping algorithm (SFLA) based on the conjugate gradient method to solve

the problem of low solution precision and easy to fall into local optimal solutions of the traditional SFLA.

Since the rules of dividing meme groups make the fitness values of the last frog subgroup too bad, which

affects the searching speed of the SFLA in the whole group, we introduce the conjugate gradient method into

the SFLA. We select the last meme groups to search the solution by using the conjugate gradient method, which

prevents the hybrid SFLA from falling into local optimal solution and improves the convergence accuracy. The

proposed hybrid SFLA algorithm combines the strongly global searing ability of the SFLA with the fast local

searching ability of the conjugate gradient method. Numerical experiment results show that the hybrid SFLA

has a higher convergent precision, more stable optimal results, and jumps out of local optimal solution

easily in comparison with the basic SFLA.

Key words: shuffled frog leaping algorithm, conjugate gradient method, numerical experiment, fitness function

PANG Kai-li，LIANG Xi-ming.

A hybrid SFLA algorithm based on conjugate gradient method

[J]. Computer Engineering & Science.

[1]	LIANG Xi-ming, SHI Lan-yan, LONG Wen. An improved snake optimization algorithm based on hybrid strategies and its application [J]. Computer Engineering & Science, 2024, 46(04): 693-706.
[2]	LIU Ting1,2,YANG Qiu-xiang1. Service composition optimization based on the fireworks algorithm mixing chaotic mechanism and Levy mutation #br# [J]. Computer Engineering & Science, 2019, 41(09): 1618-1626.
[3]	WU Jianping,YIN Fukang,PENG Jun,YANG Jinhui. Edge weight selection of the aggregation-type algebraic multigrid preconditioner from top to bottom [J]. Computer Engineering & Science, 2019, 41(02): 191-196.
[4]	HUANG Huixian，HU Pengfei. A hybrid algorithm combining conjugate gradient method and feedback differential evolution and its application in tension string design [J]. Computer Engineering & Science, 2018, 40(07): 1316-1322.
[5]	WANG Lin1,WAN Xiao-yu1,WAN Jian-chao2. A hybrid differential shuffled frog leaping algorithm based on selection strategy [J]. Computer Engineering & Science, 2018, 40(01): 121-127.
[6]	DAI Yue-ming,ZHANG Ming-ming,WANG Yan. A coevolutionary shuffled frog leaping algorithm [J]. Computer Engineering & Science, 2018, 40(01): 139-147.
[7]	XIE Xiao-jun1,2,YU Chun-qiang3,WANG Bo1,HE Xian1,XU Zhang-yan1,2. A test-cost-sensitive attribute reduction algorithm based on immune quantum particle swarm optimization [J]. Computer Engineering & Science, 2017, 39(07): 1371-1378.
[8]	CHANG Xiaogang，ZHAO Hongxing. Shuffled frog leaping algorithm based on trigonometric search factor [J]. Computer Engineering & Science, 2016, 38(11): 2362-2367.
[9]	CAO Yang,QIAN Xiaodong. A big data clustering algorithm based on local key nodes [J]. J4, 2016, 38(07): 1338-1343.
[10]	XU Guangxian,YANG Dongli，GAO Song，XU Chunyan，JIN Yubo. Combination of coding overhead and network security in the network coding optimization scheme [J]. Computer Engineering & Science, 2016, 38(07): 1384-1390.
[11]	YU Jinping1,ZHANG Yong2,LIAO Liefa2,MEI Hongbiao3. A clustering algorithm based on modified shuffled frog leaping algorithm and Kmeans [J]. J4, 2016, 38(02): 356-362.
[12]	ZHANG Kaiyuan，NING Qianzhi，NIU Tingting. A double iterative algorithm for symmetric solution todiscrete time algebraic Riccati matrix equation [J]. J4, 2015, 37(02): 329-334.
[13]	WANG Lianguo1,2，GONG Yaxing2. A single population shuffled frog leaping algorithm [J]. J4, 2014, 36(03): 463-468.
[14]	ZHANG Youhua1，WANG Lianguo2. Parameter identification of load model based on an improved shuffled frog leaping algorithm [J]. J4, 2013, 35(6): 180-185.
[15]	ZHANG Yunong,LUO Feiheng,CHEN Jinhao,LI Weibing. Weights and structure determination of 3-input Bernoulli polynomial neural net [J]. J4, 2013, 35(5): 142-148.

A hybrid SFLA algorithm based on conjugate gradient method

PDF

Knowledge

Abstract

Cite this article

share this article

Related Articles 15

Recommended Articles

Metrics

Comments