| 引用本文： |
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赵艳玲,王勇,袁磊.基于4VA信息素的蝗虫优化算法[J].广西科学,2022,29(5):930-939. [点击复制]
- ZHAO Yanling,WANG Yong,YUAN Lei.Grasshopper Optimization Algorithm Based on 4-Vinylanisole Pheromone[J].Guangxi Sciences,2022,29(5):930-939. [点击复制]
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| 摘要: |
| 针对标准蝗虫优化算法(Grasshopper Optimization Algorithm,GOA)存在的不足，基于对蝗虫活动习性和行为特征的模拟，结合GOA模型，提出一种基于4-乙烯基苯甲醚(4-vinylanisole,4VA)信息素的蝗虫优化算法(Grasshopper Optimization Algorithm Based on 4-vinylanisole Pheromone,VAGOA)。首先，基于4VA是蝗虫的聚集信息素，设计4VA信息素表达式；其次，对不同蝗虫群体(群居型蝗虫和散居型蝗虫)中的个体分别采用不同的搜索策略，在探索和开发之间取得平衡，使算法全局探索能力和局部开发能力均得到有效提升，增强算法的全局寻优能力和规避陷入局部最优的能力。通过12个基准函数的仿真实验，并与GOA、PSO、HCUGOA、SA_CAGOA算法相比较，结果表明VAGOA的全局搜索能力有明显提高，在函数优化中明显具有更快的全局收敛速度及更好的稳定性。 |
| 关键词: 蝗虫优化算法(GOA) 4-乙烯基苯甲醚(4VA)信息素 聚集搜索方法 分散搜索方法 智能优化 全局探索 局部开发 |
| DOI：10.13656/j.cnki.gxkx.20221116.014 |
| 投稿时间：2021-12-13修订日期：2022-02-07 |
| 基金项目:国家自然科学基金项目(62266007)和广西自然科学基金项目(2021GXNSFAA220068)资助。 |
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| Grasshopper Optimization Algorithm Based on 4-Vinylanisole Pheromone |
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ZHAO Yanling1, WANG Yong1,2,3, YUAN Lei1
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| (1.College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, Guangxi, 530006, China;2.Guangxi Key Laboratory of Hybrid Computation & IC Design Analysis, Nanning, Guangxi, 530006, China;3.Key Laboratory of Guangxi High Schools Complex System & Computational Intelligence, Nanning, Guangxi, 530006, China) |
| Abstract: |
| Aiming at the shortcomings of standard Grasshopper Optimization Algorithm (GOA),based on the simulation of grasshopper activity habits and behavior characteristics,combined with the GOA algorithm model,a Grasshopper Optimization Algorithm Based on 4VA (4-vinylanisole) Pheromone (VAGOA) is proposed.Firstly,based on the fact that 4VA is the grasshopper aggregation pheromone,the expression of 4VA pheromone is designed.Secondly,different search strategies are adopted for individuals in different grasshopper populations (gregarious grasshopper and scattered grasshopper) to achieve a balance between exploration and development,so that the global exploration ability and local development ability of the algorithm are effectively improved,and the global optimization ability of the algorithm and the ability to avoid falling into local optimum are enhanced.Through the simulation experiments of 12 benchmark functions and the comparison with GOA,PSO,HCUGOA and SA_CAGOA algorithms,the results show that the global search ability of VAGOA is obviously improved,and it has obviously faster global convergence speed and better stability in function optimization. |
| Key words: Grasshopper Optimization Algorithm (GOA) 4-vinylanisole (4VA) pheromone aggregated search method scattered search method intelligent optimization global exploration local exploitation |
