Aiming at the drawbacks of standard Grey Wolf Optimizer （GWO） algorithm， such as slow convergence and being easy to fall into local optimum， an improved Grey Wolf Optimizer with Two Headed Wolves guide （GWO-THW） algorithm was proposed by utilizing a dual nonlinear convergence factor strategy. Firstly， the chaotic Cubic mapping was used to initialize the population for improving the uniformity and diversity of the population distribution. And the wolves were divided into hunter wolves and scout wolves through the average fitness values. The different convergence factors were used to two types of wolves to seek after and round up their prey under the leadership of their respective leader wolf. Secondly， an adaptive weight factor of position updating was designed to improve the search speed and accuracy. Meanwhile， a Levy flight strategy was employed to randomly update the positions of wolves for jumping out of local optimum， when no prey was found in a certain period of time. Ten benchmark functions were selected to test the performance and effectiveness of GWO-THW. Experimental results show that compared with standard GWO and related variants， GWO-THW achieves higher optimization accuracy and faster convergence on eight benchmark functions， especially on the multi-peak functions， the algorithm can converge to the ideal optimal value within 200 iterations， indicating that GWO-THW has better optimization performance.

Focusing on the unbalance issue between local optimization and global optimization and the inability to jump out of the local optimum of Artificial Fish Swarm Algorithm （AFSA）， an Adaptive AFSA utilizing Gene Exchange （AAFSA-GE） was proposed. Firstly， an adaptive mechanism of view and step was utilized to enhance the search speed and accuracy. Then， chaotic behavior and gene exchange behavior were employed to improve the ability of jumping out of the local optimum and the search efficiency. Ten classic test functions were selected to prove the feasibility and robustness of the proposed algorithm by comparing it with the other three modified AFSAs， which are Normative Fish Swarm Algorithm （NFSA）， FSA optimized by PSO algorithm with Extended Memory （PSOEM-FSA）， and Comprehensive Improvement of Artificial Fish Swarm Algorithm （CIAFSA）. Experimental results show that AAFSA-GE achieves better results in local and global search ability than those of PSOEM-FSA and CIAFSA，and better search efficiency and better global search ability than those of NSFA.