In recent years, the authors have proposed an effective metaheuristic method for continuous optimization problems based on an analogy of spiral phenomena in nature. This method is called Spiral Optimization (SPO). SPO has two setting parameters: the convergence rate and the rotation rate. Depending on computational and/or problem conditions, the values of these parameters affect search performa...

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We pr...

The Covariance Matrix Adaptation Evolution Strategy (CMAES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meaning that only one hyper-parameter, the population size, is proposed to be tuned by the user. In this paper, we propose a principled approach called se...

In this paper, we present a novel approach to joint word sense disambiguation (WSD) and entity linking (EL) that combines a set of complementary objectives in an extensible multi-objective formalism. During disambiguation the system performs continuous optimization to find optimal probability distributions over candidate senses. The performance of our system on nominal WSD as well as EL improve...

A new method is introduced for packing items in convex regions of the Euclidian ndimensional space. By means of this approach the packing problem becomes a global finitedimensional continuous optimization problem. The strategy is based on the new concept of sentinels. Sentinels sets are finite subsets of the items to be packed such that, when two items are superposed, at least one sentinel of o...

For a finite undirected graph G on n vertices two continuous optimization problems taken over the n-dimensional cube are presented and it is proved that their optimum values equal the domination number γ of G. An efficient approximation method is developed and known upper bounds on γ are slightly improved.

This paper presents a structural synthesis using the Mixed-Integer Non-Linear Programming (MINLP) approach. The MINLP is a combined discrete/continuous optimization technique, where discrete binary 0-1 variables are defined for optimization of discrete alternatives and continuous variables for optimization of parameters. The MINLP optimization to a structural synthesis is performed through thre...

Self-adaptation is becoming a standard method for optimizing mutational parameters within evolutionary programming. The majority of these efforts have been applied to continuous optimization problems. This paper offers a preliminary investigation into the use of self-adaptation for discrete optimization using the traveling salesman problem. Two self-adaptive approaches are analyzed. The results...

This paper focuses on the optimization of hyper-parameters for function approximators. We describe a kind of racing algorithm for continuous optimization problems that spends less time evaluating poor parameter settings and more time honing its estimates in the most promising regions of the parameter space. The algorithm is able to automatically optimize the parameters of a function approximato...

This paper presents a solution to the global optimization of continuous functions by the Differential Ant-Stigmergy Algorithm (DASA). The DASA is a newly developed algorithm for continuous optimization problems, utilizing the stigmergic behavior of the artificial ant colonies. It is applied to the high-dimensional real-parameter optimization with low number of function evaluations. The performa...