An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

Abstract

The firefly algorithm (FA) has become one of the most prominent swarm intelligence methods due to its efficiency in solving a wide range of various real-world problems. In this paper, an upgraded firefly algorithm (UFA) is proposed to further improve its performance in solving constrained engineering optimization problems. The main modifications of the basic algorithm are the incorporation of the logistic map and reduction scheme mechanism in order to perform fine adjustments of its control parameters, and employing a mutation operator in order to provide useful diversity in the population. Also, the proposed approach uses certain feasibility-based rules in order to guide the search to the feasible region of the search space, the improved scheme to handle the boundary constraints and the method for handling equality constraints. The UFA is tested on a set of 24 benchmark functions presented in CEC'2006 and nine widely used constrained engineering optimization problems. Comprehensive experimental results show that the overall performance of the UFA is superior to the FA and its recently proposed variants. Moreover, it achieves highly competitive results compared with other state-of-the-art metaheuristic techniques.

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

“…Costa et al [34] used penalty based techniques to evaluate different test functions for global optimization with FA. Brajevic et al [35] developed feasibility-rule based with FA for COPs. Kulkarni et al [36] proposed a modified feasibility-rule based for solving COPs using probability.…”

“…Kulkarni et al [36] proposed a modified feasibility-rule based for solving COPs using probability. The upgraded FA (UFA) is proposed to solve mechanical engineering optimization problem [37]. Chou and Ngo designed a multidimensional optimization structure with modified FA (MFA) [38].…”

“…To investigate the performance and effectiveness of the SRIFA, these results are compared with five metaheuristic algorithms. These algorithms are stochastic ranking with a particle-swarm-optimization (SRPSO) [46], self adaptive mix of particle-swarm-optimization (SAMO-PSO) [47], upgraded firefly algorithm (UFA) [37], an ensemble of constraint handling techniques for evolutionary-programming (ECHT-EP2) [48] and a novel differential-evolution algorithm (NDE) [49]. To evaluate proper comparisons of these algorithms, the same number of function evaluations (NFEs = 240,000) were chosen.…”

Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

“…The two important characteristics of metaheuristic search algorithm which play a crucial role in achieving global optima are exploitation and exploration. Many researchers have proposed several methods [24][25][26][27][28] to equalize them to improve the performance of metaheuristic algorithms. Recently, a paradigm of mathematics known as ''chaos theory'' is combined with the domain of stochastic optimization algorithms to increase the efficiency.…”

An improved chaotic firefly algorithm for global numerical optimization

“…The FA is effective metaheuristic technique, which has simple concept and easy implementation. Therefore the FA has been studied by many researchers and new FA variants have been described to solve different classes of optimization problems, such as continuous, combinatorial, constrained, multi-objective, dynamic and noisy optimization 20,21,22,23,24 .…”