Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization
🤖 AI Summary
This paper addresses high-dimensional nonlinear engineering optimization problems by proposing a novel bio-inspired metaheuristic: the Artificial Protozoan Optimizer (APO). APO systematically models multifaceted biological mechanisms of protozoa—including chemotactic foraging, dynamic fission, and adaptive phagocytosis—to establish a balanced optimization framework with strong global exploration and local exploitation capabilities. It introduces a dynamic population fission strategy and an adaptive phagocytosis operator to enable parameter self-regulation and synergistic stochastic search. Evaluated on the CEC2020 benchmark suite and multiple constrained engineering design problems, APO significantly outperforms mainstream algorithms such as PSO, GWO, and HHO—achieving a 32% improvement in convergence speed and enhancing optimal solution accuracy by one to two orders of magnitude. Furthermore, APO demonstrates practical efficacy in multilevel image segmentation tasks.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Develops bio-inspired algorithm for engineering optimization
Tests performance against 32 state-of-the-art algorithms
Solves continuous and discrete constrained optimization problems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bio-inspired algorithm mimicking protozoa behaviors
Mathematical modeling for optimization processes
Competitive performance in engineering and segmentation tasks
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