Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, the understanding of the mechanisms that make swarms so successful is still limited. We present the first substantial experimental investigation of the influence of the local attractor on the quality of exploration and exploitation. We compare in detail classical PSO with the social-only variant where local attractors are ignored. To measure the exploration capabilities, we determine how frequently both variants return results in the neighborhood of the global optimum. We measure the quality of exploitation by considering only function values from runs that reached a search point sufficiently close to the global optimum and then comparing in how many digits such values still deviate from the global minimum value. It turns out that the local attractor significantly improves the exploration, but sometimes reduces the quality of the exploitation. As a compromise, we propose and evaluate a hybrid PSO which switches off its local attractors at a certain point in time. The effects mentioned can also be observed by measuring the potential of the swarm.
Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization
Department of Biomedical Engineering, Case Western Reserve University（生物医学工程系，凯斯西储大学）；Department of Electrical and Computer Engineering, Virginia Commonwealth University（电气与计算机工程系，弗吉尼亚 Commonwealth 大学）
Discrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States
Business School, Shandong Normal University（山东师范大学商学院）；School of Mathematics, Yunnan Normal University（云南师范大学数学学院）；School of Physics and Electronics, Shandong Normal University（山东师范大学物理与电子科学学院）
Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis
Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）；Department of Mathematics and Computing, Dr. B.R. Ambedkar National Institute of Technology Jalandhar（德拉·B·R·阿姆贝卡尔国家理工学院贾兰德哈尔分校数学与计算系）；KINDI Computing Research Center, College of Engineering, Qatar University（卡塔尔大学工程学院KINDI计算研究中心）
Dynamic Multi-Objective Lion Swarm Optimization with Multi-strategy Fusion: An application in 6R robot trajectory planning
College of control science and engineering, China University of Petroleum (East China)（中国石油大学（华东）控制科学与工程学院）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织（CSIRO））
Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization
University of Technology Sydney (UTS)（悉尼科技大学）；VNU University of Engineering and Technology (VNU-UET), Vietnam National University, Hanoi (VNU)（越南国立大学河内分校工程技术大学）
Bayesian optimization of distributed neurodynamical controller models for spatial navigation
Johns Hopkins University/Applied Physics Laboratory（约翰斯·霍普金斯大学/应用物理实验室）；Kavli Neuroscience Discovery Institute, Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
