All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37\% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68\% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization
VŠB-Technical University of Ostrava（俄斯特拉发技术大学）；Torrens University Australia（澳大利亚托伦斯大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Amman Arab University（安曼阿拉伯大学）
Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for non-convex global optimization
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
A Random-Key Optimizer for Combinatorial Optimization
Federal U. of São Paulo（巴西圣保罗联邦大学）；Amazon Advanced Solutions Lab（亚马逊高级解决方案实验室）；DIMACS；University of Southampton（南安普顿大学）；Federal U. of Pernambuco（巴西佩雷布鲁克联邦大学）
Applying a Random-Key Optimizer on Mixed Integer Programs
Federal Univ. of São Paulo（圣保罗联邦大学）；DIMACS（离散数学与计算机科学中心）；Instituto Federal de Santa Catarina（圣卡塔琳娜联邦学院）；North（55北）；Amazon Advanced Solutions Lab（亚马逊高级解决方案实验室）；AWS Center for Quantum Computing（AWS量子计算中心）
A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, lightweight, and earthquake-resistant reinforced concrete cantilever retaining walls
School of Information and Physical Sciences, The University of Newcastle（新castle大学信息与物理科学学院）；Department of Civil Engineering, University of Memphis（Memphis大学土木工程系）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼技术大学工程与信息技术学院）
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization
Huazhong University of Science and Technology（华中科技大学）
大多数现实世界中的优化问题往往会出现多个全局最优或局部最优。因此，越来越多的小生境元启发式算法，致力于寻找多个最优在一个单一的运行，被开发来解决这些多峰优化问题。然而，现有的小生境元启发式算法存在两个亟待解决的难题：如何针对不同的优化问题设置小生境参数的最优值，以及如何有效地跳出局部最优。这两个难点在很大程度上限制了它们的实用性。本文在前人提出的鲸群算法（WSA）的基础上，提出了一种新的多峰优化算法WSA with Iterative Counter（WSA-IC）。WSA-IC一方面改进了WSA的迭代规则，避免了对不同问题指定不同的衰减系数以形成多个子种群的需要，同时也不引入任何小生境参数。另一方面，WSA-IC使得能够在迭代期间依赖于两个新参数（即，稳定性阈值Ts和适应性阈值Tf），以跳出所定位的极值点。同时证明了WSA-IC的收敛性。最后，在CEC 2015小生境基准测试函数和另外5个高维经典多峰函数上，将该算法与几种小生境元启发式算法进行了比较.实验结果表明，WSA-IC在统计上优于其他小生境元启发式算法对大多数测试函数。
An improved educational competition optimizer with multi-covariance learning operators for global optimization problems
Institute of Artificial Intelligence Application, Ningbo Polytechnic（宁波职业技术学院人工智能应用研究所）；Zhicheng College, Fuzhou University（福州大学至诚学院）；School of Artificial Intelligence, Ningbo Polytechnic（宁波职业技术学院人工智能学院）；Faculty of Electronic Engineering & Technology, University Malaysia Perlis（马来西亚玻璃市大学电子工程技术学院）；School of Electrical Automation and Information Engineering, Tianjin University（天津大学电气自动化与信息工程学院）
A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer
Helwan University（赫勒万大学）；University of Sharjah（沙迦大学）；Mansoura University（曼苏拉大学）；Middle East University（中东大学）；Al Hussein Technical University（侯赛因技术大学）；University of Granada（格拉纳达大学）
Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization
Halfway Escape Optimization: A Quantum-Inspired Solution for General Optimization Problems
School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（西交利物浦大学人工智能与高级计算学院）；School of Engineering and Technology, Sunway University（双威大学工程与技术学院）
