As a mean to provide reactive power compensation to radial distribution networks, capacitor banks are commonly installed within these electrical distribution systems. When properly allocated, capacitor banks provide several benefits, including an improvement on the feeder's voltage profile and significant reductions on power loss, which, in turn, leads to important amounts of energy savings and cost reductions. This formulation is known in the literature as optimal capacitor placement (OCP) problem. From an optimization perspective, OCP is considered remarkably complex due to its high multi-modality, discontinuity, and non-linearity. In this paper, the swarm optimization algorithm known as locust search (LS) is proposed for solving the OCP problem. The proposed approach has been tested by considering several IEEE's radial distribution test systems, and its performance has also been compared against that of other techniques currently reported on the literature to solve the OCP problem. Our experimental results suggest that the proposed LS-based method is able to competitively solve the OCP problem in terms of accuracy and robustness.

In this paper, a novel swarm algorithm called locust search (LS) is proposed for solving optimisation tasks. The LS algorithm is based on the simulation of the behaviour presented in swarms of locusts. In the proposed algorithm, individuals emulate a group of locusts which interact to each other based on the biological laws of the cooperative swarm. Experimental results demonstrate a high performance of the proposed method for searching a global optimum in comparison with other well-known evolutionary methods.

As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1) they frequently obtain suboptimal results (misclassifications) as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2) the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS), is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness.