In this work a new method based on geometric fractal decomposition to solve large-scale
continuous optimization problems is proposed. It consists of dividing the feasible search …

This paper proposes a multi-scale search algorithm for solving global optimization problems
given a finite number of function evaluations. We refer to this algorithm as the Naive Multi …

This paper presents a new fractal search space decomposition-based algorithm to address
the issue of scaling up the divide and conquer approach to deal with large scale problems …

Global optimization problems are considered in which the objective functions and
constraints can be expensive black-box multi-extremal functions satisfying the Lipschitz …

The complexity of Pareto fronts imposes a great challenge on the convergence analysis of
multi-objective optimization methods. While most theoretical convergence studies have …

Random embedding has been applied with empirical success to large-scale black-box
optimization problems with low effective dimensions. This paper proposes the …

An adaptation of the oscars algorithm for bound constrained global optimization is
presented, and numerically tested. The algorithm is a stochastic direct search method, and …

In this paper, a new multi-objective optimization algorithm in a multi-scale framework with
faster convergence characteristics is presented, referred to as the Pareto-Aware DIviding …

Stochastic adaptive Fourier decomposition (SAFD) is a recently developed sparse
representation theory that combines the traditional signal decomposition method with …

This last decade the complexity of the problems increased with the increase of the CPUs'
power and the decrease of memory costs. The appearance of clouds infrastructures provide …