LS-II: AN IMPROVED LOCUST SEARCH (NEW VERSION)

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several state-of the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness. The code implements this algorithm.

More details can be seen in:

Octavio Camarena, Erik Cuevas, Marco Pérez-Cisneros, Fernando Fausto, Adrián González, and Arturo Valdivia. Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems, Mathematical Problems in Engineering, Volume 2018, Article ID 4148975, 15 pages

https://www.hindawi.com/journals/mpe/2018/4148975/