Artificial locust swarm optimization

Optimization is generally the process of finding the best and aims to find an acceptable solution to a problem defined over a given search space. As the artificial intelligence has developed, meta-heuristic algorithms have become popular for solving optimization problems. Meta-heuristic algorithms are particularly inspired by biological systems and swarm behavior in nature. Since it is not possible for just one method to solve all optimization problems, many meta-heuristic algorithms have been developed and continue to be developed in the literature. In this regard, in this thesis, a new swarm intelligence based meta-heuristic algorithm called Artificial Locust Swarm Optimization (ALSO) is proposed inspiring 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 tested on 22 benchmark functions and 3 engineering design problems and compared with other recent and well-known optimization algorithms. Simulation results prove that the proposed ALSO algorithm is quite competitive when compared to the other algorithms. Moreover, it even requires the less runtime and memory space under the same conditions.