Emerson Hochsteiner de Vasconcelos Segundo

Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization

Social-spider optimization (SSO) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, which is based on the simulation of cooperative behavior of social spiders. To enhance the performance of the standard SSO, a modified SSO (MSSO) approach based on beta distribution and natural gradient local search was proposed in this paper. In order to verify the performance of the MSSO, tests using Loneys solenoid benchmark and a brushless direct current motor benchmark are realized comparing the effectiveness of the SSO and the proposed MSSO. The results of this paper demonstrated that the MSSO performance is promising in electromagnetics optimization.

Multiobjective Krill Herd Algorithm for Electromagnetic Optimization

Metaheuristics have recently become the forefront of the current research as a powerful way to deal with many electromagnetic optimization problems. Based on the simulation of the herding behavior of krill individuals, a krill herd (KH) algorithm was recently proposed to solve optimization problems. In order to extend the classical mono-objective KH algorithm approach, this paper proposes a new multiobjective KH (MOKH) algorithm and a modified MOKH approach using the beta distribution in the inertia weight tuning. Numerical results on a multiobjective constrained brushless direct current motor design problem show that the evaluated MOKH algorithms present a promising performance.

A modified gravitational search algorithm for continuous optimization

The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.

Multiobjective wind driven optimization approach applied to transformer design

Metaheuristics of the natural computing field have been proposed as an alternative to mathematical optimization approaches to address non convex problems involving large search spaces. In recent years a new optimization metaheuristic algorithm was proposed called Wind Driven Optimization (WDO). WDO is a stochastic nature-inspired paradigm based on atmospheric motion. In this paper, a modified version of WDO is proposed and evaluated, based on Lévy flights (or Lévy motions) to tune its control parameters, called Lévy WDO (LWDO). Lévy flight or anomalous diffusion process is a random walk characterized by Markov chain in which the step-lengths have a probability distribution that is heavy-tailed. To evaluate the multiobjective optimization performance of the WDO and the proposed LWDO, a benchmark for optimizing of a safety isolating transformer is adopted. In this paper, the transformer design optimization is treated as a multiobjective problem, with the aim to minimize both the total mass (iron and copper materials) and losses taking into consideration design constraints. Simulation results testify that the multiobjective LWDO is a promising approach for multiobjective optimization as it outperforms the WDO in multiobjective version and the classical NSGA-II (Non-dominated Sorting Genetic Algorithm, version II).

A Zaslavskii firefly approach applied to Loney's solenoid benchmark

Nature-inspired algorithms of the swarm intelligence field perform powerfully and efficiently in solving global optimization problems. Inspired by nature, these metaheuristic algorithms have obtained promising performance over continuous domains of optimization problems. Recently, a new swarm intelligence approach called firefly algorithm (FA) has emerged. The FA is a stochastic paradigm based on the idealized behavior of the flashing characteristics of fireflies. However, to achieve good performance with FA, the tuning of control parameters is essential as its performance is sensitive to the choice of the randomization parameter (α) setting. This paper introduces a FA approach combined with chaotic sequences generated by Zaslavskii map (FACZ) to tune the randomization parameter. Simulations of Loneys solenoid benchmark problem examine the effectiveness of the conventional FA and the proposed FACZ algorithms. Simulation results and comparisons with the FACZ demonstrated that the performance of the FA is promising in the Loneys solenoid case.

A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design

The classical particle swarm optimization (PSO) algorithm is inspired on biological behaviors such as the social behavior of bird flocking and fish schooling. In this context, many significant improvements related the updating formulas and new operators have been proposed to improve the performance of the PSO algorithm in the literature. On the other hand, recently, as an alternative to the classical PSO, a quantumbehaved particle swarm optimization (QPSO) algorithm was proposed. The contribution of this paper is linked with a modified QPSO based on beta probability distribution and mutation differential operator. The effectiveness of the proposed modified QPSO algorithm is demonstrated by solving three kinds of optimization problems including two benchmark functions and a circular antenna design problem.

Enhanced Flower Pollination Approach Applied to Electromagnetic Optimization

It is difficult to use the deterministic mathematical tools such as a gradient method to solve global optimization problems. Flower pollination algorithm (FPA) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, based on the characteristics of flowering plants. To enhance the performance of the standard FPA, an enhanced FPA (EFPA) approach based on beta probability distribution was proposed in this paper. In order to verify the performance of the proposed EFPA, five benchmark functions are chosen from the literature as the test suit. Furthermore, tests using Loney’s solenoid benchmark, a classical problem in the electromagnetics area, are realized to evaluate the effectiveness of the FPA and the proposed EFPA. Simulation results and comparisons with the FPA demonstrated that the performance of the EFPA approach is promising in electromagnetics optimization.

A Wind Driven Approach Using Lévy Flights for Global Continuous Optimization

Recently, the metaheuristics have drawn a great attention to researchers. The drawbacks of existing derivative-based numerical methods have forced the researchers to rely on metaheuristics founded on simulations to solve scientific computation and engineering optimization problems. A common feature shared by the metaheuristics is that they combine rules and randomness to imitate some natural phenomena. Wind driven optimization (WDO) belongs to optimization metaheuristic algorithm. It is a stochastic nature-inspired global optimization method based on atmospheric motion. In this paper, we focus our study on an enhanced WDO using Levy flights (WDOLE) applied to global optimization in the continuous domain. To evaluate the performance of the proposed WDOLE, well-known unconstrained benchmark functions in the literature are optimized using the proposed WDOLE, and provides comparisons with the standard WDO.