The ant colony optimization algorithm is a classical swarm intelligence algorithm, but it cannot be used for continuous class optimization problems. A continuous ant colony optimization algorithm (ACOR) is proposed to overcome this difficulty. Still, some problems exist, such as quickly falling into local optimum, slow convergence speed, and low convergence accuracy. To solve these problems, this paper proposes a modified version of ACOR called ADNOLACO. There is an opposition-based learning mechanism introduced into ACOR to effectively improve the convergence speed of ACOR. All-dimension neighborhood mechanism is also introduced into ACOR to further enhance the ability of ACOR to avoid getting trapped in the local optimum. To strongly demonstrate these core advantages of ADNOLACO, with the 30 benchmark functions of IEEE CEC2017 as the basis, a detailed analysis of ADNOLACO and ACOR is not only qualitatively performed, but also a comparison experiment is conducted between ADNOLACO and its peers. The results fully proved that ADNOLACO has accelerated the convergence speed and improved the convergence accuracy. The ability to find a balance between local and globally optimal solutions is improved. Also, to show that ADNOLACO has some practical value in real applications, it deals with four engineering problems. The simulation results also illustrate that ADNOLACO can improve the accuracy of the computational results. Therefore, it can be demonstrated that the proposed ADNOLACO is a promising and excellent algorithm based on the results.

Harris hawks optimization (HHO) has been accepted as one of the well-established swarm-based methods in the community of optimization and machine learning that primarily works based on multiple dynamic features and various exploratory and exploitative traits. Compared with other optimization algorithms, it has been observed that HHO can obtain high-quality solutions for continuous and constrained complex and real-world problems. While there is a wide variety of strategies in the HHO for dealing with diverse situations, there are chances for sluggish performance, where the convergence rate can gradually slow with time, and the HHO may stay stuck in the current relatively better place and may be unable to explore other better areas. To mitigate this concern, this paper combines the Cauchy mutation mechanism into the HHO algorithm named CMHHO. This idea can boost performance and provide a promising optimizer for solving complex optimization problems. The Cauchy mutation mechanism can speed up the convergence of the solution and help HHO explore more promising regions compared to its basic release. On 30 IEEE CEC2017 benchmark functions, the study compared the proposed CMHHO with various conventional and advanced metaheuristics to validate its performance and quality of solutions. It has been found through experiments that the overall optimization performance of CMHHO is far superior to all competitors. The CMHHO method is applied to four engineering challenges to investigate the capabilities of the proposed algorithm in solving real-world problems, and experimental results show that the suggested algorithm is more successful than existing algorithms.