Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors: the market in which it operates, the size of the time window, and others. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series. We propose the combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike other approaches, this paper applies a set of different MOEAs, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of non-dominated solutions obtained with different MOEAs simultaneously. Experimental results show that this technique increases the returns of the commonly used Buy \& Hold strategy and other multi-objective strategies, even for daily operations.
Training-Free Diffusion-Driven Modeling of Pareto Set Evolution for Dynamic Multiobjective Optimization
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究所）；Department of Artificial Intelligence, School of Informatics, Xiamen University（厦门大学信息学院人工智能系）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education（教育部多媒体可信感知与高效计算重点实验室）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室）；Department of Artificial Intelligence, Sichuan University（四川大学人工智能系）
A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores
School of Computer Science, South China Normal University（华南师范大学计算机学院）；National-local Joint Engineering Laboratory for Digitalize Eletrical Design Technology, Wenzhou University（温州大学数字电气设计技术国家地方联合工程实验室）；Institute of Science and Technology Innovation, Dongguan University of Technology（东莞理工学院科技创新研究院）；College of Cyber Security, Guangzhou University（广州大学网络空间安全学院）
Proven Advantage of Multiobjective Evolutionary Algorithms for Problems with Different Degrees of Conflict
School of Computer Science and Technology, International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳，计算机科学与技术学院，人工智能国际研究院）
Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm
School of Automation and Electrical Engineering, Zhongyuan University of Technology（中原文理大学自动化与电气工程学院）；School of Electrical Engineering, Zhengzhou University（郑州大学电气工程学院）
RDEx-MOP: Indicator-Guided Reconstructed Differential Evolution for Fixed-Budget Multiobjective Optimization
Department of Engineering, University of Toyama（富山大学工学部）；Cyberscience Center, Tohoku University（东北大学网络科学中心）；Faculty of Science and Technology, Hirosaki University（弘前大学理工学部）；Sino-German College of Applied Sciences, Tongji University（同济大学中德工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；Department of Information Engineering, Yantai Vocational College（烟台职业学院信息工程系）
