A Survey of Collaborative of Swarm Intelligence for Evolutionary Computation
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摘要: 演化计算为代表的群体智能的迅速发展引发了人工智能领域新一轮技术变革。为满足多样化复杂系统应用需求，人工智能越来越趋向于跨级别的智能化、协同化研究。该文提出面向演化计算的群智协同的概念，根据群智协同层级将人工智能跨级别的智能化、协同化研究分为微观协同、中观协同与宏观协同，以群智协同视角对近年来上述分支领域相关研究做出了总结。首先，通过分析决策变量级协同、全局与局部级协同对微观协同进行了阐述。其次，从目标级协同和任务级协同两个维度对中观协同进行了总结。再次，以智能协同系统中存在的空天地海协同、车路云协同和端边云协同对宏观协同展开分析。最后，该文指出了面向演化计算的群智协同领域的研究挑战，并对相关领域发展方向进行了展望。Abstract: The rapid development of swarm intelligence, represented by evolutionary computation, has triggered a new wave of technological transformation in the field of artificial intelligence. To meet the diverse application needs of complex systems, artificial intelligence is increasingly moving towards cross-level intelligent and collaborative research. In this paper, the concept of swarm intelligence cooperation oriented towards evolutionary computation is proposed. Based on the hierarchical levels of swarm intelligence cooperation, artificial intelligence research across different levels is categorized into micro-level cooperation, meso-level cooperation, and macro-level cooperation. From the perspective of swarm intelligence cooperation, a summary is provided on recent research in the aforementioned branches. Firstly, the micro-level cooperation is discussed by analyzing decision variable level cooperation and global/local level cooperation. Secondly, the meso-level cooperation is summarized from the dimensions of objective-level cooperation and task-level cooperation. Furthermore, an analysis of macro-level cooperation is conducted through the examination of space-air-ground-sea cooperation, vehicle-road-cloud cooperation, and edge-cloud cooperation in intelligent collaborative systems. Finally, the research challenges in the field of swarm intelligence cooperation oriented towards evolutionary computation are identified, and future directions for related fields are proposed.
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Key words:
- Collaborative optimization /
- Swarm intelligence /
- Evolutionary computation
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表 1 决策变量级协同方法的研究总结
算法 策略 算法 策略 决策变量分组过程中 CPSO-SL[32] 固定部分决策变量为常数，动态调整部分决策变量取值 DECC-II[26] 预定义范围内动态调整
分组大小HCMPSO[39] 增益函数衡量解的贡献程度，
结合增益函数评估适应度MLCC[27] 自适应调整策略 DSMGA[36] 保留子组份中的优质解 CCFA[28], CCAS[29] 动态的子组份和子种群大小 FEA[31] ${R_i}$在完整解中计算适应度 FDA[33] 因子分布 DIMA[40] 信息交换机制 BMDA[34] 2元边际分布算法 演化算法完整解整合过程中 MIMIC[35] 简单链式分布 CPSO[38] 依据权重比例组合子组份 FEA[31] 重叠分组策略 DSMGA[36] 自动创建定制化重组算子 DSMGA[36] 依赖矩阵聚类 CPSO-SL[32] 优化后的子问题核心决策变量 演化算法优化评估过程中 FEA[31] 最佳适应度个体整合 CCGA[37], CPSO[38], DECC-II[26] 固定部分决策变量为最优值，
动态调整部分决策变量取值DIMA[40] 信息交流机制 
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表 2 全局与局部级协同方法的研究总结
算法 策略 算法 策略 以种群初始化的形式进行 Hybrid GA[48] 专门化交叉算子 DE+Opposition learning [42] 反向学习初始化 随机性形式的局部搜索策略 GA[43] 混合初始化和顺序变换 Tabu search+MA[49] 禁忌搜索 对问题进行预处理 AGLMA[50] 模拟退火 GTSP reduction algorithms [44] GTSP约简技术 HCS+MOEA[51] 局部帕累托集 MOGLS[46] 对模糊规则预筛选 自适应的局部搜索策略 确定性形式的局部搜索策略 SFMDE[52] 自适应选择 BS+MA[47] 分支界限法 SGMA[53] 模因和基因协同进化
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表 3 目标级协同方法的研究总结
算法 策略 算法 策略 最优求解过程中 preferred +EA[73] 偏好(favour)关系 Goldberg[57] 基于Pareto的排序 $\varepsilon $-preferred+EA[74] 偏好关系(修改) 种群多样性维持过程中 基于偏好信息的协同 NPGA[65] 目标域适应度共享 DM+MOEA[77] MOP中引入偏好表达方法 Srinivas[66] 决策变量域适应度共享 NSGA-II[78] 参考点偏好信息(NSGA-II) 基于修改Pareto支配的协同 基于目标数量调节的协同 NSGA-II[71] 改良Pareto部分支配 PCA-NSGA-II[84] 基于PCA的目标简化 基于偏好关系的协同 SIBEA[85] 基于Pareto支配关系调节
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表 4 任务级协同方法的研究总结
算法 策略 算法 策略 知识迁移时机 基于任务相似度衡量 MFEA[102] 历代跨任务知识迁移 CTFDC[125] 最佳解间距、适应度等级相关性、
适应度场景分析协同衡量EMT[103] 固定世代间隔知识迁移 MFEA[126] 协同度量($\xi $) MT-CPSO[104] 触发式知识迁移 任务选择 知识迁移内容 SaEF-AKT[128] KL散度衡量任务间进行选择 EMT[108] 非支配原则选择优质解 MSSTO[129] Wasserstein距离衡量相似性 知识迁移方式 group-based MFEA[130] 相似性信息与反馈信息结合 MFEA[102] 种群间模拟二进制交叉 AMTEA[131], TEMO-MPSs[132] 混合模型参数作任务选择概率 MFEA-Direction Vector[111] 搜索方向沿用 Symbiosis in Biocoenosis Optimization[133] 粗粒度方法自适应控制 EMT[103] 自编码器学习任务间线性映射 任务间知识迁移 MOMFEA-SADE[113] 子空间对齐策略 GMFEA[134] 个体均匀中心与样本均值之差 EMT-LTR[114] 学习任务关系 MFEA-Genetic transform strategy and Hyper-rectangle search strategy[135] 源域减后目标域加样本均值 ASCMFDE[115] 决策空间低维子空间的对齐矩阵 LDA-MFEA[136] 最小二乘法构建映射矩阵 AMTEA+${M_S}$[137] 神经网络对个体进行非线性匹配
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