An improved firefly algorithm for numerical optimization problems and it’s application in constrained optimization

2021 ◽  
Author(s):  
Kamran Rezaei ◽  
Hassan Rezaei
Keyword(s):  
Constrained Optimization ◽  
Numerical Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Improved Firefly Algorithm

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

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 250 ◽  
Author(s):  
Umesh Balande ◽  
Deepti Shrimankar
Keyword(s):  
Constrained Optimization ◽  
Engineering Design ◽  
Real World ◽  
Optimization Design ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Design Problems ◽  
Improved Firefly Algorithm ◽  
Engineering Design Problems ◽  
And Performance

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

scholarly journals Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yue Wu ◽  
Qingpeng Li ◽  
Qingjie Hu ◽  
Andrew Borgart
Keyword(s):  
Topology Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Structural Topology ◽  
Discrete Variables ◽  
And Topology ◽  
Design Variables ◽  
Improved Firefly Algorithm ◽  
Discrete Design Variables

Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short) is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

scholarly journals IFA-EO: An Improved Firefly Algorithm Hybridized With Extremal Optimization for Continuous Unconstrained Optimization Problems

2021 ◽  
Author(s):  
Min-Rong Chen ◽  
Liu-Qing Yang ◽  
Guo-Qiang Zeng ◽  
Kang-Di Lu ◽  
Yi-Yuan Huang
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Local Optimum ◽  
Step Size ◽  
Extremal Optimization ◽  
Unconstrained Optimization Problems ◽  
Improved Firefly Algorithm ◽  
Attraction Model ◽  
Complex Optimization ◽  
Adaptive Step

Abstract As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

A new and efficient firefly algorithm for numerical optimization problems

2018 ◽  
Vol 31 (5) ◽  
pp. 1445-1453 ◽  
Author(s):  
Xiuqin Pan ◽  
Limiao Xue ◽  
Ruixiang Li
Keyword(s):  
Numerical Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems

scholarly journals Enhanced Firefly Algorithm Using Fuzzy Parameter Tuner

2018 ◽  
Vol 11 (1) ◽  
pp. 26 ◽  
Author(s):  
Mahdi Bidar ◽  
Samira Sadaoui ◽  
Malek Mouhoub ◽  
Mohsen Bidar
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Exploration And Exploitation ◽  
Improved Firefly Algorithm ◽  
Engineering Problems ◽  
Exploitation And Exploration ◽  
Low Dimensional ◽  
Fuzzy Parameter

Exploitation and exploration are two main search strategies of every metaheuristic algorithm. However, the ratio between exploitation and exploration has a significant impact on the performance of these algorithms when dealing with optimization problems. In this study, we introduce an entire fuzzy system to tune efficiently and dynamically the firefly algorithm parameters in order to keep the exploration and exploitation in balance in each of the searching steps. This will prevent the firefly algorithm from being stuck in local optimal, a challenge issue in metaheuristic algorithms. To evaluate the quality of the solution returned by the fuzzy-based firefly algorithm, we conduct extensive experiments on a set of high and low dimensional benchmark functions as well as two constrained engineering problems. In this regard, we compare the improved firefly algorithm with the standard one and other famous metaheuristic algorithms. The experimental results demonstrate the superiority of the fuzzy-based firefly algorithm to standard firefly and also its comparability to other metaheuristic algorithms.

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