A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.

Adaptive Gibbs Sampling (AGS) algorithm is a new heuristic for unconstrained global optimization. AGS algorithm is a population-based method that uses a random search strategy to generate a set of new potential solutions. Random search combines the one-dimensional Metropolis-Hastings algorithm with the multidimensional Gibbs sampler in such a way that the noise level can be adaptively controlled according to the landscape providing a good balance between exploration and exploitation over all search space. Local search strategies can be coupled to the random search methods in order to intensify in the promising regions. We have performed experiments on three well known test problems in a range of dimensions with a resulting testbed of 33 instances. We compare the AGS algorithm against two deterministic methods and three stochastic methods. Results show that the AGS algorithm is robust in problems that involve central aspects which is the main reason of global optimization problem difficulty including high-dimensionality, multi-modality and non-smoothness.

In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objective is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chemical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural 1 technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which generates zones with orthogonal shapes, e.g., L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is e cient to solve real-field and randomly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricultural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.

This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.