The purpose of this paper is to present an algorithm, called Differential Ant-Stigmergy Algorithm (DASA), for global optimization of high-dimensional real-parameter cost functions. The DASA outperformed the included differential evolution type algorithm in convergence on all test functions and also obtained better solutions on some test functions. The DASA may find applications in challenging real-life optimization. The DASA is one of the first ACO-based algorithms proposed for global optimization of the high-dimensional real-parameter problems.
COBISS.SI-ID: 22592039
This article presents an optimization method used at the electric-motor design. The goal of the optimization was to find the geometrical parameter values that would generate the rotor and the stator geometries with minimum power losses. Due to a time-consuming solution evaluation the sequential algorithm is inefficient in terms of time. For this reason, a new, efficient distributed implementation of the MASA is presented. In addition, we have shown that with distributed computing the computation time can be drastically reduced without any noticeable reduction in the quality of the solution.
COBISS.SI-ID: 21585191
In this paper we propose a promising optimization algorithm, referred to as the Multilevel Ant Stigmergy Algorithm (MASA), which exploits stigmergy in order to optimize multi-parameter functions. We evaluate the performance of the MASA and Differential Evolution - one of the leading stochastic method for numerical optimization - in terms of their applicability as numerical optimization techniques. The comparison is performed using several widely used benchmark functions with added noise.
COBISS.SI-ID: 21826087
This paper presents a solution to the global optimization of continuous functions by the Differential Ant-Stigmergy Algorithm (DASA). It is applied to the high-dimensional real-parameter optimization with low number of function evaluations. The performance of the DASA is evaluated on the set of 25 benchmark functions provided by CEC’2005 Special Session on Real Parameter Optimization. Furthermore, non-parametric statistical comparisons with eleven state-of-the-art algorithms demonstrate the effectiveness and efficiency of the DASA.
COBISS.SI-ID: 22568743
In this chapter we will present so-called Differential Ant-Stigmergy Algorithm (DASA), a new approach to the continuous optimization problem. The chapter starts with the DASA description followed by three case studies which show real-world application of the proposed optimization approach. Finally, we conclude with discussion of the obtained results. We have shown that the DASA is capable of efficiently solving different complex real-world optimization problems.
COBISS.SI-ID: 23111975