Bitterling Colony Optimization: a bio-inspired algorithm for global search

ZP Lai and JB Ding and HF Yin and LJ Wu and J Liu and W He, CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 28, 542 (2025).

DOI: 10.1007/s10586-025-05170-x

In recent years, the growing complexity of optimization problems has highlighted the limitations of existing metaheuristic algorithms, such as premature convergence and insufficient exploration-exploitation balance. This paper proposes a novel bio-inspired algorithm, Bitterling Colony Optimization (BCO), inspired by the synergistic relationship between a bitterling colony and mussels during the breeding season. The mathematical model is developed to characterize pairing, searching for mussels to reproduce, evicting, and traveling behaviors of bitterling colony. Detailed parameter analysis enhances solution diversity, improves convergence reliability, and balances exploration and exploitation. Experimental analysis is conducted on 23 commonly used test functions, the CEC2017 test suite, and four constrained engineering optimization problems to demonstrate the proposed algorithm's effectiveness, stability, and usability. Performance testing of BCO is done with success-history-based adaptive DE with linear population size reduction (LSHADE), Kepler meerkat naked (KMN), Starling murmuration optimizer (SMO), Quantum-based avian navigation optimizer algorithm (QANA), Whale optimization algorithm (WOA), Multi-verse optimizer (MVO), Salp swarm algorithm (SSA), Harris hawks optimization (HHO), and Aquila optimizer (AO). We conducted the Friedman test, Wilcoxon Signed-Rank Test (WSRT) and Mean Absolute Error (MAE) test regarding statistics. According to experimental and statistical results, BCO ranks 1 in comprehensive performance on 23 commonly used test functions and 100-dimensional CEC2017 test suite. In addition, BCO can find the optimal value in four constrained engineering problems. As a result, BCO is competitive in various optimization problems.

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