Intelligent optimization algorithm is an important artificial intelligence method which is often used to solve complex black-box optimization problems. From the perspective of the nature of algorithm performance, this report will describe the fundamental reasons and key points of algorithm performance improvement. It will introduce the idea of micro-scale searching algorithm: By determining the effective decision subset of optimization problems, adjust the reasonable allocation of computational resources and achieve effective search in a small space, thus obtaining the optimal solution or high-quality feasible solution of the problem. Based on this algorithmic idea, The report will analyze the micro-scale searching assumptions and performance nature of intelligent optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO). It will also summarize and discuss the pitfalls of analyzing performance of evolutionary computing methods. Finally, this report will introduce the applications of micro-scale searching algorithm ideas in industrial software, software engineering, computer vision, digital logistics, etc.

Han Huang is a professor and doctoral supervisor of the School of Software Engineering at South China University of Technology. He holds a PhD in computer science from South China University of Technology. Prof. Huang has been elected for the National Youth Talent Project. He is currently serving as an associate editor of IEEE Transaction on Evolutionary Computation (IF: 16.497) and Complex & Intelligent Systems (IF: 6.700), associate director of the Key Laboratory of Big Data and Intelligent Robots of the Ministry of Education, associate director of Intelligent Simulation Optimization and Scheduling Special Committee, and Director of Teaching Steering Committee for Software Engineering of Undergraduate Colleges and Universities in Guangdong Province. He is also a CCF Distinguished Member and IEEE Senior Member. Prof. Huang has Presided over nearly 20 national and provincial projects including Key Research and Development Program of the Ministry of Science and Technology of China, General Projects funded by National Natural Science Foundation of China, Guangdong Natural Science Funds for Distinguished Young Scholar, etc. He has published more than 60 papers as the first author and corresponding author in IEEE TCYB, IEEE TETC, IEEE TSE, IEEE TEVC, IEEE TIP, IEEE TFS, and Science China, including ESI highly cited papers. He has 36 invention patents granted in China and six invention patents granted in the United States. He won first prize in the Guangdong Scientific and Technological Progress Awards, second prize in the Guangdong Natural Science Awards, Excellent Paper Award of China Simulation Federation, and second prize of Science and Technology Progress of China Society of Image and Graphics. Prof. Huang has been committed to the research on the theories, applications and industrial ecology of intelligent algorithms. He also released five public software systems such as Evolutionary Algorithm Time Complexity Analysis System (www.eatimecomplexity.net), Automatic Generation System of Structural Equation Model (www.autosem.net), and Energy Storage Planning Simulation and Market Development System (http://energystorage.autosem.net). He has completed more than 70 practical application cases of intelligent algorithm technologies.