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.