Integrated optimization algorithm: a metaheuristic approach for complicated optimization

• Main Authors: Li, Chen, Chen, Guo, Liang, Gaoqi, Luo, Fengji, Zhao, Junhua, Dong, Zhao Yang
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Non-Convex Optimization; Non-Differentiable Optimization
• Online Access: https://hdl.handle.net/10356/163884
• Institution: Nanyang Technological University

This paper proposes an integrated optimization algorithm (IOA) designed for solving complicated optimization problems that are non-convex, non-differentiable, non-continuous, or computationally intensive. IOA is synthesized from 5 sub-algorithms: follower search, leader search, wanderer search, crossover search, and role learning. The follower search finds better solutions by tracing the leaders. The leader search refines current optimal solutions by approaching or deviating from the central point of the population and then executes a single-round coordinate descent. The wanderer search carries out comprehensive search space expansion. The crossover search generates offspring using solutions from superior parents. Role learning automates the process in which a search agent decides whether to become a follower or a wanderer. A global optima estimation framework (GOEF) is proposed to offer guidelines for designing an efficient optimization algorithm, and IOA is proved to attain global optima. A differentiable integrated optimization algorithm (DIOA) that extends gradient descent is put forward to train deep learning models. Empirical case studies conclude that IOA shows a much faster convergence speed and finds better solutions than the other 8 comparative algorithms based on 27 benchmark functions. IOA has also been applied to solve unit commitment problems in the power system and shows satisfactory results. A power line sub-image classification model based on a convolutional neural network (CNN) is optimized by DIOA. Compared with the pure gradient descent approach, DIOA converges significantly faster and obtains a high test set accuracy with much fewer training epochs.