Artificial locust swarm optimization algorithm.

This study proposes a new metaheuristic algorithm, which is called Artificial Locust Swarm Optimization (ALSO), inspired by random jumping and plant invasion behavior of locust swarms. Locusts interact in two different ways of searching for food: social and familial. In the familial phase, small locust groups search foods in a local area and the locusts share their information in the social phase. The proposed algorithm is less likely to trap into the local solution than other methods and has high performance in the sensitivity of the global solution. In addition, it is effective not only for the solution of black-box optimization problems but also for the solution of problems with an irregular objective function. The ALSO algorithm is compared with other recent and well-known optimization algorithms on 22 benchmark functions and 3 real engineering design problems. Simulation results prove that the ALSO algorithm is very competitive when compared to the other algorithms. Moreover, it even requires the less runtime and memory space under the same conditions. [ABSTRACT FROM AUTHOR]