Research Article Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems Octavio Camarena, Erik Cuevas , Marco Pérez-Cisneros , Fernando Fausto, Adrián González, and Arturo Valdivia Departamento de Electr´ onica, Universidad de Guadalajara, CUCEI Av. Revoluci´ on 1500, 44430 Guadalajara, Mexico Correspondence should be addressed to Erik Cuevas; erik.cuevas@cucei.udg.mx Received 29 March 2018; Revised 30 August 2018; Accepted 30 September 2018; Published 16 October 2018 Guest Editor: Eduardo Rodriguez-Tello Copyright © 2018 Octavio Camarena et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te 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. Tese interesting search schemes allow LS to overcome some of the difculties that commonly afect 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. Te most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modifcation to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modifed 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 the 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. 1. Introduction For the last few decades, optimization approaches inspired by the natural collective behavior of insects and animals have captivated the attention of many researchers. Tese techniques, commonly referred to as swarm optimization methods, combine deterministic rules and randomness with the purpose of mimicking some kind of natural phenomena, typically manifested in the form of a swarm behavior. Search strategies based in swarm behaviors have demonstrated to be adequate to solve complex optimization problems, ofen delivering signifcantly better solutions than those produced by traditional methods. Currently, an extensive variety of swarm-based optimization techniques can be found on the literature. Some examples include Particle Swarm Optimization (PSO), which emulates the social behavior of focking birds or fshes [1], the Artifcial Bee Colony (ABC) approach, which considers the cooperative behavior mani- fested in bee colonies [2], the Cuckoo Search (CS) algorithm, which simulates the brood parasitism behavior manifested by cuckoo birds [3], the Firefy Algorithm (FA), which mimics the distinctive bioluminescence-based behavior observed in frefies [4], among others. Te Locust Search (LS) [5] algorithm is a swarm opti- mization approach inspired in the biological behavior of the desert locusts (Schistocerca gregaria). Biologically, locusts experiment two opposite phases: solitary and social. In the solitary phase, locusts avoid contact with others conspecifcs in order to explore promising food sources. In opposition, in the social phase, locusts frantically aggregate around abundant foods sources (such as plantations) devastating them. Tis aggregation is carried on through the attraction to those elements that are found the best food sources. By integrating these two distinctive behaviors, LS maintains powerful global and local search capacities which enable it to solve efectively a wide range of complex optimization problems such as image processing [6], parameter estimation of chaotic systems [7], pattern recognition [8], among others. Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 4148975, 15 pages https://doi.org/10.1155/2018/4148975