Engineering System Design Using Firefly Algorithm and Multi-Objective Optimization

Abstract

Modern engineering problems, such as aircraft or automobile design, are often composed by a large number of variables that must be chosen simultaneously for better design performance. Normally, most of these parameters are conflicting, i.e., an improvement in one of them does not lead, necessarily, to better results for the other ones. Thus, many methods to solve multi-objective optimization problems (MOP) have been proposed. The MOP solution, unlike the single objective problems, is a set of non-dominated solutions that form the Pareto Curve, also known as Pareto Optimal. Among the MOP algorithms, we can cite the Firefly Algorithm (FA). FA is a bio-inspired method that mimics the patterns of short and rhythmic flashes emitted by fireflies in order to attract other individuals to their vicinities. For illustration purposes, in the present contribution the FA, associated with the Pareto dominance criterion, is applied to three different design cases. The first one is related to the geometric design of a clamped-free beam. The second one deals with the project of a welded beam and the last one focuses on estimating the characteristic parameters of a rotary dryer pilot plant. The proposed methodology is compared with other evolutionary strategies. The results indicate that the proposed approach characterizes an interesting alternative for multi-objective optimization problems.

Robust Multi-objective Optimization Applied to Engineering Systems Design

“…This optimization strategy is based on the Effective Mean Concept -EMC (DebandGupta, 2006) in association with the Multi-objective Optimization Firefly Colony Algorithm -MOFCA (Lobato et al, 2011). The organization of this article is as follows.…”

“…Due to the success obtained by the FCA in different science and engineering applications (Apostolopoulos and Vlachos, 2011;Lukasik and Zak, 2009;Yang, 2009;Pfeifer and Lobato, 2010), Lobato et al (2011) proposed the Multi-objective Optimization Firefly Colony Algorithm (MOFCA). This approach is based on the classical FCA associated with Fast Non-Dominated Sorting and has the following structure:…”

“…In this sense we can cite the development of evolutionary optimization methodologies based on genetic algorithms (Castro, 2001;Deb, 2001;Djamaluddin et al 2015); fish swarm (Lobato and Steffen Jr.); bee colony (GhashochiBargh and Sadr, 2014) and firefly colony (Yang, 2008;Yang, 2009;Pfeifer and Lobato, 2010;Lobato et al 2011), to cite few. The Firefly Colony Algorithm (FCA)is based on strategies that seek to mimic the behavior of fireflies observed in the nature to update a population of candidates to solve optimization problems.…”

A comparative study of the application of differential evolution and simulated annealing in radiative transfer problems

“…DE has been successfully applied to various fields such as digital filter design (Storn, 1995), batch fermentation process (Chiou and Wang, 1999), estimation of heat transfer parameters in a bed reactor (Babu and Sastry, 1999), synthesis and optimization of heat integrated distillation system (Babu and Singh, 2000), optimization of an alkylation reaction (Babu and Gaurav, 2000), parameter estimation in fed-batch fermentation process (Wang et al, 2001), optimization of thermal cracker operation (Babu and Angira, 2001), engineering system design (Lobato and Steffen, 2007), economic dispatch optimization (Coelho and Mariani, 2007), identification of experimental data (Maciejewski et al, 2007), apparent thermal diffusivity estimation during the drying of fruits (Mariani et al, 2008), besides other applications (Storn et al, 2005).…”

A Comparative Study Using Shuffled Complex Evolution and Differential Evolution Applied to Robotic Manipulator Design

“…DE has been successfully tested in various fields, such as: solution of multi-objective optimal control problems with index fluctuation applied to fermentation process [17], digital filter design [33], multi-objective optimization of mechanical structures [18], solution of inverse radiative transfer problems in two-layer participating media [19], estimation of drying parameters in rotary dryers [20], apparent thermal diffusivity estimation of the drying of fruits [25], Gibbs free energy minimization in a real system [21], estimation of space-dependent single scattering albedo in radiative transfer problems [22,23,24], design of fractional order PID controllers [5], and other applications [9,31,32].…”