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The sequential adding clustering algorithm herein presented adds cluster centers, assigns points to clusters, merges and removes clusters (deleting their centers) iteratively in order to find the optimal cluster partition. Additionally, the algorithm optimizes the cluster center positions by minimizing the 'sum of within-cluster distances'. The preliminary clustering results show that the algorithm is effective and efficient. [ABSTRACT FROM AUTHOR]

Build orientation is a critical issue in Additive manufacturing (AM), where three-dimensional objects are created layerby- layer directly from a 3D CAD model, since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduce, significantly, the building costs and will improve the object accuracy. This work presents the solutions obtained by the Genetic Algorithm (GA) in solving the part build orientation optimization problem, considering the staircase effect, support area characteristics and the building time of four models. Preliminary experiments show that GA gives competitive results in solving the build orientation problem when compared with other metaheuristics. [ABSTRACT FROM AUTHOR]

An extension of the firefly algorithm (FA) for solving mixed-integer nonlinear programming (MINLP) problems is presented. Although penalty functions are nowadays frequently used to handle integrality conditions and inequality and equality constraints, this paper proposes the implementation within the FA of a simple rounded-based heuristic and four preference rules to find and converge to MINLP feasible solutions. Preliminary numerical experiments are carried out to validate the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the 'erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature. [ABSTRACT FROM AUTHOR]

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered. [ABSTRACT FROM AUTHOR]

In this paper, we present a derivative-free multilevel coordinate search (MCS) approach, that relies on the Hooke and Jeeves local search, for globally solving bound constrained optimization problems. Numerical experiments show that the proposed algorithm is effective in solving benchmark problems, when compared with the well-known solvers MCS and DIRECT. [ABSTRACT FROM AUTHOR]

The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods. [ABSTRACT FROM AUTHOR]

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and population size. It is reported that DE is sensitive to the choice of these parameters. To improve the quality of the solution, in this paper, we propose a modified differential evolution introducing self-adaptive parameters, modified mutation and the inversion operator. We test our method with a set of nonlinear continuous optimization problems with simple bounds. [ABSTRACT FROM AUTHOR]