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A new hybrid algorithm is proposed by incorporating Harris Hawks Optimization with Marine Predators algorithm and dynamic Opposition-based learning, namely DMPA-HHO. In the algorithm, the problem is addressed that Harris Hawks Optimization (HHO) tends to fall into local optima and low accuracy of the solution. Dynamic Opposite Learning (DOL) improves the swarm diversity and swarm quality, and enhances the global search capability and search accuracy. HHO and the Marine Predators Algorithm (MPA) are blended to enhance the progressive rapid dives of the Harris hawk flock, effectively improving the algorithm's exploitation capabilities. DMPA-HHO uses the FADs’ effect of the MPA to increase the possibility of individuals escaping from the local optimum solution when the search falls into the local optimal solution. Compared with others on several benchmark functions, the DMPA-HHO algorithm has a better search accuracy and a stronger ability to avoid trapping in local optima.

To improve the performance of the whale optimization algorithm and further enhance the search accuracy, while increasing the convergence speed, a quasi-reflective chaotic mutant whale swarm optimization, namely QNWOA, is proposed, fused with an operator of Fish Aggregating Devices (FADs) in this paper. Firstly, the swarm diversity is increased by using logistic chaotic mapping. Secondly, a quasi-reflective learning mechanism is introduced to improve the convergence speed of the algorithm. Then, the FADs vortex effect and wavelet variation of the marine predator algorithm (MPA) are introduced in the search phase to enhance the stability of the algorithm in the early and late stages and the ability to escape from the local optimum by broking the symmetry of iterative routes. Finally, a combination of linearly decreasing and nonlinear segmentation convergence factors is proposed to balance the local and global search capabilities of the algorithm. Nine benchmark functions are selected for the simulation, and after comparing with other algorithms, the results show that the convergence speed and solution accuracy of the proposed algorithm are promising in this study.