Daniel Leal Souza

PSO-GPU: accelerating particle swarm optimization in CUDA-based graphics processing units

This work presents a PSO implemention in CUDA architecture, aiming to speed up the algorithm on problems which has large amounts of data. PSO-GPU algorithm was designed to customization, in order to adapt for any problem that can be solved by a PSO algorithm. By implementing PSO using CUDA architecture, each processing core of the GPU will be responsible for a portion of the overall processing operation, where each one of these pieces are handled and executed in a massive parallel enviroment, opening the possibility for solving problems that require a large processing load in considerably less time. In order to evaluate the performance of PSO-GPU algorithm two functions were used, both global optimization problems, where without constraints (Griewank function) and other considering constraints, the Welded Beam Design (WBD).

A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization

This paper presents a new Cooperative Evolutionary Multi-Swarm Optimization Algorithm (CEMSO-GPU) based on CUDA parallel architecture applied to solve engineering problems. The focus of this approach is: the use of the concept of master/slave swarm with a mechanism of data sharing; and, the parallelism method based on the paradigm of General Purpose Computing on Graphics Processing Units (GPGPU) with CUDA architecture, brought by NVIDIA corporation. All these improvements were made aiming to produce better solutions in fewer iterations of the algorithm and to improve the search for best results. The algorithm was tested for some well-known engineering problems (WBD, ATD, MWTCS, SRD-11) and the results compared to other approaches.

ABC+ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark Functions

The following paper introduces a hybrid algorithm that combines Artificial Bee Colony Algorithm (ABC) and a model of Evolution Strategies (ES) found in the Evolutionary Particle Swarm Optimization (EPSO), another hybrid metaheuristic. The goal of this approach is to incorporate the effectiveness and simplicity of the ABC with the thorough local search mechanism of the Evolution Strategies in order to devise an algorithm that is able to achieve better optimality in less time than the original ABC applied to function optimization problems. With the intention of assessing this novel algorithm performance and reliability, several unconstrained benchmark functions as well as four large-scale constrained optimization-engineering problems (WBD, DPV, SRD-11 and MWTCS) act as an evaluation environment. The results obtained by the ABC+ES are compared to original ABC and several other optimization techniques.

ABC+ES: a novel hybrid artificial bee colony algorithm with evolution strategies

This paper has the purpose of presenting a new hybridization of the Artificial Bee Colony Algorithm (ABC) based on the evolutionary strategies (ES) found on the Evolutionary Particle Swarm Optimization (EPSO). The main motivation of this approach is to augment the original ABC in a way that combines the effectiveness and simplicity of the ABC with the robustness and increased exploitation of the Evolution Strategies. The algorithm is intended to be tested on two large-scale engineering design problem and its results compared to other optimization techniques.

A Novel Competitive Quantum-Behaviour Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Constrained Engineering Design

This paper presents a new bio-inspired algorithm named Competitive Quantum-Behaviour Evolutionary Multi-Swarm Optimization (CQEMSO) based on CUDA parallel architecture applied to solve engineering problems, using the concept of master/slave swarm working under a competitive scheme and being executed over the paradigm of General Purpose Computing on Graphics Processing Units (GPGPU). The efforts on implementing the CQEMSO algorithm are focused at generating a solution which includes greater quality of search and higher speed of convergence by using mechanisms of evolutionary strategies with the procedures of search and optimization found in the classic QPSO. For performance analysis, the proposed solution was submitted to some well-known engineering problems (WBD, DPV) and its results compared to other solutions found on scientific literature.

Evolutionary Quick Artificial Bee Colony for Constrained Engineering Design Problems

The Artificial Bee Colony (ABC) is a well-known simple and efficient bee inspired metaheuristic that has been showed to achieve good performance on real valued optimization problems. Inspired by such, a Quick Artificial Bee Colony (QABC) was proposed by Karaboga to enhance the global search and bring better analogy to the dynamic of bees. To improve its local search capabilities, a modified version of it, called Evolutionary Quick Artificial Bee Colony (EQABC), is proposed. The novel algorithm employs the mutation operators found in Evolutionary Strategies (ES) that was applied in ABC from Evolutionary Particle Swarm Optimization (EPSO). In order to test the performance of the new algorithm, it was applied in four large-scale constrained optimization structural engineering problems. The results obtained by EQABC are compared to original ABC, QABC, and ABC + ES, one of the algorithms inspired for the development of EQABC.

A PSO FPGA based architecture to optimize constrained functions

The search for optimal solutions in Science and Technology is one of the greatest challenges, because performance optimization, especially in complex systems, is often ruled with antagonistic variables such as budget constraints, security requirements and real-time responses. In light of this, many system optimization techniques are constantly being developed and presented to the scientific community. This paper proposes a parallels hardware architecture with FPGA (Field-Programmable Gate Arrays) that can make use of the parallel features that the Swarm Intelligence algorithm PSO (Particle Swarm Optimization) offers to mitigate the engineering Design Pressure Vessel (DPV) problem under constraints. We believe our proposed hardware architecture can be used as an alternative to the PSO software solution because its results are very promising when compared to those obtained by others papers which have relied on software as a solution platform.

Design and optimization of new sub-THz compact switch based on 2D photonic crystal with quadrupole resonance in ferrite resonator

This paper presents the design and optimization of a new switch, based on a two-dimensional photonic crystal with rectangle lattice consisting of a resonant cavity and two waveguides for use in sub-terahertz band. The resonant cavity is based on a ferrite cylinder biased by an external DC magnetic field with a quadrupole mode in the resonator. In order to enlarge the bandwidth of the switch, we applied an optimization procedure by using BOBYQA algorithm for search and optimization of geometrical parameters for the resonant cavity and dielectric rods, as well as the magnetic field intensity.