A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems

As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.

Automated summary

As science and technology advance, more complex engineering problems emerge, increasing the need for new optimization techniques. The cuckoo search algorithm has been widely used but can no longer meet current optimization requirements. This paper proposes an improved cuckoo search algorithm called CCFCS, which incorporates a crossover optimizer and a decentralized foraging strategy to enhance its search ability and ability to escape local optima. The algorithm's performance is verified through various tests and it shows faster convergence and higher solution quality compared to other algorithms.