A Bacterial Chemostatic-Based Bio-Inspired Algorithm for the Structural Optimization of Planar Structures

Abstract

Topology optimization using bio-inspired algorithms has gained significant attention in recent decades, particularly for problems involving non-derivative search spaces. This paper introduces a novel bio-inspired algorithm for structural topology optimization, termed the bacterial chemotaxis-based topology optimization algorithm (BCBTOA). Inspired by bacterial chemotaxis, the method models material removal as a guided search process within a two-dimensional continuous domain, with the objective of minimizing structural compliance. To address common numerical challenges such as checkerboarding and mesh dependency, a chemotaxis-based regularization scheme is implemented. The algorithm requires only a single control parameter, R, which governs cavity size and material distribution. Numerical experiments on benchmark problems demonstrate that BCBTOA generates distinct, binary layouts without gray regions and produces mesh-independent solutions for optimal R values. Several communication models between artificial bacteria were investigated, with a modified exponential model yielding the best performance. In addition, different initialization strategies were evaluated, showing that random initialization consistently produced topologies with lower compliance. Overall, the algorithm achieves performance comparable to established methods such as sequential element rejection and admission (SERA) and soft bidirectional evolutionary structural optimization (BESO).