To overcome premature convergence and local minimum of Ant Lion Optimizer (ALO) algorithm, an innovative ALO algorithm based on self-adaptive chaos search which is combined with Tent chaos algorithm is proposed. Tent mapping is applied to initialize individuals in the search space. Self-adaptive adjustment of chaos search scopes is presented to get the better solution, perfect the fitness of the worse individuals and enhance the whole individuals' fitness and efficiency. At the same time, Tournament selection strategy is used to screen out ant lion individuals. At the end, ants' random walking behavior is optimized by chaos operator to form a parallel search mode with ant lion foraging behavior, which is beneficial to the overall optimization performance to obtain the optima. Algorithm's performance is tested through complex benchmark functions with high-dimension and path planning problem. Experimental results of the former further confirm that, as for benchmark functions with 30 dimensions, it costs about 0.5 s to converge to the optimum, and for 50 dimensions, it is about 2 s. Compared with ALO and other optimization algorithms, the improved algorithm not only accelerates the convergence rate and improves the precision, but it is also fit for complex high-dimensional functions optimization. Path planning test manifests that for airspace with 7 menaces, while the search dimension is 10, after 0.939 s, and 30 iterations, the global optimum of the path cost function can be got. Compared with ALO, it has advantage in speed and accuracy to get the specific path, and it is of great value in actual problems.