Information acquisition optimizer: a new efficient algorithm for solving numerical and constrained engineering optimization problems

Abstract

This paper addresses the increasing complexity of challenges in the field of continuous nonlinear optimization by proposing an innovative algorithm called information acquisition optimizer (IAO), which is inspired by human information acquisition behaviors and consists of three crucial strategies: information collection, information filtering and evaluation, and information analysis and organization to accommodate diverse optimization requirements. Firstly, comparative assessments of performance are conducted between the IAO and 15 widely recognized algorithms using the standard test function suites from CEC2014, CEC2017, CEC2020, and CEC2022. The results demonstrate that IAO is robustly competitive regarding convergence rate, solution accuracy, and stability. Additionally, the outcomes of the Wilcoxon signed rank test and Friedman mean ranking strongly validate the effectiveness and reliability of IAO. Moreover, the time comparison analysis experiments indicate its high efficiency. Finally, comparative tests on five real-world optimization difficulties affirm the remarkable applicability of IAO in handling complex issues with unknown search spaces. The code for the IAO algorithm is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/169331-information-acquisition-optimizer.