An improved metaheuristic algorithm called the Crossover strategy integrated Secretary Bird Optimization Algorithm (CSBOA) is proposed in this work for solving real optimization problems. This improved algorithm integrated logistic-tent chaotic mapping initialization, an improved differential mutation operator, and crossover strategies with the Secretary Bird Optimization Algorithm (SBOA) for a better quality solution and faster convergence. To evaluate the performance of CSBOA, two sets of a standard benchmark set, CEC2017 and CEC2022, were applied first. The Wilcoxon rank sum test and Friedman test were also used to statistically compare the proposed CSBOA algorithm with seven common metaheuristics. The comparisons demonstrated that CSBOA is more competitive than other metaheuristic algorithms on most benchmark functions. Additionally, the performance of CSBOA was validated for two challenging engineering design case studies. Comparative results showed that CSBOA provides more accurate solutions than the SBOA and the other seven algorithms, suggesting viability in dealing with real global optimization problems.

To address the limitations of the artificial lemming algorithm (ALA) in convergence accuracy and premature convergence, this paper proposes an enhanced variant, the cross-strategy-integrated artificial lemming algorithm (CALA). Specifically, the CALA integrates a linear inertia weight strategy to balance exploration and exploitation, a historical best-guided strategy to enhance local search, and a crossover strategy to maintain population diversity. The proposed algorithm was evaluated on the IEEE CEC2017 and CEC2022 benchmark suites, achieving minimum Friedman mean ranks of 1.37 and 1.5, respectively, and outperforming several state-of-the-art algorithms in terms of accuracy and robustness. Furthermore, the CALA was successfully applied to constrained engineering design problems and a photovoltaic model parameter estimation task, demonstrating its effectiveness and practical applicability.

This paper presents a novel hybrid algorithm that combines the Butterfly Optimization Algorithm (BOA) and Quantum-behavior Particle Swarm Optimization (QPSO) algorithms, leveraging $gbest$ to establish an algorithm communication channel for cooperation. Initially, the population is split into two equal subgroups optimized by BOA and QPSO respectively, with the latter incorporating the Lévy flight for enhanced performance. Subsequently, a hybrid mechanism comprising a weight hybrid mechanism, a elite strategy, and a diversification mechanism is introduced to blend the two algorithms. Experimental evaluation on 12 benchmark test functions and the Muskin model demonstrates that the synergy between BOA and QPSO significantly enhances algorithm performance. The hybrid mechanism further boosts algorithm performance, positioning the new algorithm as a high-performance method. In the Muskingum model experiment, the algorithm proposed in this article can give the best sum of the square of deviation (SSQ) and is superior in the comparison of other indicators. Overall, through benchmark test function experiments and Muskin model evaluations, it is evident that the algorithm proposed in this paper exhibits strong optimization capabilities and is effective in addressing practical problems.