基于透镜反向学习和差分进化的帝国竞争改进算法

doi:10.19678/j.issn.1000-3428.0069309

摘要/Abstract

摘要：

针对帝国竞争算法(ICA)收敛过快导致求解高维复杂问题容易陷入局部最优以及全局寻优能力不足等问题, 提出一种基于透镜反向学习和差分进化的帝国竞争改进算法LODE-IICA。首先, 引入透镜反向学习差分进化机制, 周期性地为算法种群提供新的进化方式和平衡各个帝国势力, 帮助算法种群跳出局部最优; 其次, 将精英保留策略植入到算法演化中, 重新分配殖民地, 维持种群多样性; 最后, 引入动态同化系数, 协调算法在不同阶段探索, 提高算法的稳定性。仿真实验中, 采用标准函数测试集、CEC2017测试集及CEC2020测试集检验LODE-IICA在多个维度下对不同类型函数的寻优能力。选取在标准函数测试集、CEC2017测试集和CEC2020测试集中具有代表性的15种改进算法与LODE-IICA进行实验结果比较, 结果显示, LODE-IICA引入的机制在大多数情况下有效地提高了算法性能, 同时具备较好的收敛速度和寻优能力。

关键词: 帝国竞争算法, 透镜反向学习, 差分进化, 精英保留, 同化系数

Abstract:

As the Imperialist Competitive Algorithm (ICA) converges rapidly, solutions of high-dimensional complex problems easily fall into local optima, resulting in an insufficient global optimization ability. To address this issue, this paper proposes an improved ICA Algorithm Based on Lens Opposition-based Learning and Differential Evolution (LODE-IICA). First, a dynamic lens opposition-based learning differential evolution mechanism is introduced to periodically provide new evolutionary approaches and balance the forces of various empires for the algorithmic populations to help them jump out of the local optimum. Second, elite preservation strategies are introduced in the algorithmic evolution to redistribute colonies for maintaining population diversity. Finally, dynamic assimilation coefficients are introduced to coordinate the algorithm in different stages of exploration and improve its stability. In the simulation experiments, the standard function test set and the CEC2017 and CEC2020 test sets are used to examine the ability of LODE-IICA to find the optima of different types of functions under multiple dimensions. 15 improved algorithms representative of the standard function test set and the CEC2017 and CEC2020 test sets are selected, and the experimental results are compared with the LODE-IICA results. The results show that the mechanism introduced by LODE-IICA is effective in improving the performance of the algorithm in most cases, along with a better convergence speed and optima-finding ability.

Key words: Imperialist Competitive Algorithm (ICA), lens opposition-based learning, differential evolution, elite preservation, assimilation coefficients

李斌, 潘智成. 基于透镜反向学习和差分进化的帝国竞争改进算法[J]. 计算机工程, 2025, 51(6): 155-173.

LI Bin, PAN Zhicheng. Improved Imperialist Competitive Algorithm Based on Lens Opposition-based Learning and Differential Evolution[J]. Computer Engineering, 2025, 51(6): 155-173.

https://www.ecice06.com/CN/Y2025/V51/I6/155

图/表 17

图1 透镜成像原理图

Fig.1 The diagram of lens imaging principle

图2 国家分区与聚类示意图

Fig.2 The schematic diagram of country partitioning and clustering

图3 各个算法在30维CEC2017测试集部分函数收敛曲线

Fig.3 Convergence curves of partial functions for various algorithms on the 30-dimensional CEC2017 test set

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