Firefly Algorithm, Stochastic Test Functions and Design Optimisation

Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality, and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.Comment: 12 pages, 11 figure

Experiments with firefly algorithm

Firefly Algorithm (FA) is one of the recent swarm intelligence methods developed by Xin-She Yang in 2008 [12]. FA is a stochastic, nature-inspired, meta- heuristic algorithm that can be applied for solving the hardest optimization problems. The main goal of this paper is to analyze the influence of changing some parameters of the FA when solving bound constrained optimization problems. One of the most important aspects of this algorithm is how far is the distance between the points and the way they are drawn to the optimal solution. In this work, we aim to analyze other ways of calculating the distance between the points and also other functions to com- pute the attractiveness of fireflies. To show the performance of the proposed modified FAs a set of 30 benchmark global optimization test problems are used. Preliminary experiments reveal that the obtained results are competitive when comparing with the original FA version.Fundação para a Ciência e a Tecnologia (FCT

Optimum design of purlin systems used in steel roofs

In this study, one existing purlin system which is used in steel roof is optimized by taking into account less cost and bearing maximum load via developed software. This software runs with firefly algorithm which is one of the recent stochastic search techniques. One of the metaheuristic techniques, so-called firefly algorithm imitates behaviors of natural phenomena. Behaviors and communications of firefly are inspired by this algorithm. In optimization algorithm, steel sections, distance between purlins, tensional diagonal braces are determined as design variables. Design loads are taken into account by considering TS498-1997 (Turkish Code) in point of place where structure will be built, outside factors and used materials. Profile list in TS910 is used in selection stage of cross sections of profile. Constraints of optimization are identified in accordance with bending stress, deformation and shear stress in TS648. Design variables of optimization are selected as discrete variables so as to obtain applicable results. Developed software is tested on existing real sample so; it is evaluated with regard to design and performance of algorithm

Algoritmo Luciérnaga para optimización de layout de distribución en planta

This paper shows the result of a research about the applications of bio-inspired algorithms in the field of production engineering in the Distrital University Francisco José de Caldas, covering the topics of industrial layout distribution in manufacturing plant layout. It is intended to seek the optimization of some problems of those fields, using artificial intelligence from the implementation of a firefly algorithm as metaheuristic planning tool and optimization of layout problem. With the goal of finding the best spatial allocation of work stations or cells. Theoretical concepts explored and results are presented. First, a state-of-the-art review on the subject was made, and then the possible solution algorithms were evaluated to identify the objective function to be optimized, to finally apply the firefly algorithm, and evaluate the results of performance against the Initial layout as the plant.Este trabajo muestra el resultado de una investigación sobre las aplicaciones de los algoritmos bioinspirados en el campo de la ingeniería de producción en la Universidad Distrital Francisco José de Caldas, abarcando los temas de distribución de layout industrial en planta de fabricación. Se pretende buscar la optimización de algunos problemas de dichos campos, utilizando la inteligencia artificial a partir de la implementación de un algoritmo de luciérnaga como herramienta metaheurística de planificación y optimización del problema de layout. Con el objetivo de encontrar la mejor asignación espacial de los puestos de trabajo o celdas. Se presentan los conceptos teóricos explorados y los resultados obtenidos. Primero se hizo una revisión del estado del arte sobre el tema, y luego se evaluaron los posibles algoritmos de solución para identificar la función objetivo a optimizar, para finalmente aplicar el algoritmo de la luciérnaga, y evaluar los resultados de desempeño frente al layout Inicial como la planta

Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine

By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency

Performance Evaluation of Different Optimization Algorithms for Power Demand Forecasting Applications in a Smart Grid Environment

AbstractThis paper presents an in-depth performance evaluation of three different optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO), and firefly (FF) algorithm for power demand forecasting in a deregulated electricity market and smart grid environments. In this framework, this paper proposes a hybrid intelligent algorithm for power demand forecasts using the combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network that is optimized by using FF optimization algorithm. The effectiveness and accuracy of the proposed hybrid WT+FF+FA model is trained and tested utilizing the data obtained from ISO-NE electricity market

Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical Clustering

Recommendation Systems finds the user preferences based on the purchase history of an individual using data mining and machine learning techniques. To reduce the time taken for computation Recommendation systems generally use a pre-processing technique which in turn helps to increase high low performance and over comes over-fitting of data. In this paper, we propose a hybrid collaborative filtering algorithm using firefly and agglomerative hierarchical clustering technique with priority queue and Principle Component Analysis (PCA). We applied our hybrid algorithm on movielens dataset and used Pearson Correlation to obtain Top N recommendations. Experimental results show that the our algorithm delivers accurate and reliable recommendations showing high performance when compared with  existing algorithms

Application of Firefly Algorithm and Its Parameter Setting for Job Shop Scheduling

AbstractJob shop scheduling problem (JSSP) is one of the most famous scheduling problems, most of which are categorisedinto Non-deterministic Polynomial (NP) hard problem. The objectives of this paper are to i) present the application of a recent developed metaheuristic called Firefly Algorithm (FA) for solving JSSP; ii) investigate the parameter setting of the proposed algorithm; and iii) compare the FA performance using various parameter settings. The computational experiment was designed and conducted using five benchmarking JSSP datasets from a classical OR-Library. The analysis of the experimental results on the FA performance comparison between with and without using optimised parameter settings was carried out. The FA with appropriate parameters setting that got from the experiment analysis produced the best-so-far schedule better than the FA withoutadopting parameter settings

IK-FA, a new heuristic inverse kinematics solver using firefly algorithm

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.Peer ReviewedPostprint (author's final draft