| In recent years,meta-heuristic algorithm has been widely used in the field of artificial intelligence and has become an indispensable part of the field of artificial intelligence.Meta-heuristic algorithm is often used in global optimization problems.Due to its advantages of simple principle,fewer adjustment parameters and simple programming implementation,more and more scholars devote themselves to the research in this field.Ant Lion Optimizer(ALO)is a new meta-heuristic optimization algorithm in recent years,which has been favored by many scholars because of its excellent search ability.However,ALO algorithm also has some defects,such as long running time,local optimal stagnation and immature convergence.Many articles have proposed the improvement of the Ant Lion Optimization algorithm.The improved algorithms proposed in these articles have effectively improved the convergence accuracy of the algorithm.However,most of these new improved algorithms lead to more lengthy running time.In this paper,chaos mechanism and adaptive update strategy are combined into the framework of Ant Lion Optimization algorithm,and a new algorithm,called self-adaptive Ant Lion Algorithm with Chaos Mechanism(SCALO),is proposed.The initial value is generated by chaos mechanism and the adaptive boundary is used to limit the migration of ants during random walk.This algorithm not only enhances the optimization ability of the original ALO algorithm,but also shortens the running time.We use 20 benchmark functions to test the performance of the proposed algorithm and compare it with ALO itself,ALO variant algorithms and traditional meta-heuristic optimization algorithms.Finally,we apply SCALO to engineering problems to test its practical ability.The experimental results show that the SCALO algorithm is superior to the original ALO algorithm in terms of optimization ability and running time,and has strong competitiveness compared with other meta-heuristic algorithms. |
PDF Full Text Request