Firefly Algorithm: Recent Advances and Applications

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.Comment: 15 page

Parameterisation of a Maxwell model for transient tyre force by means of an extended firefly algorithm

Developing functions for advanced driver assistance systems requires very accurate tyre models, especially for the simulation of transient conditions. In the past, parametrisation of a given tyre model based on measurement data showed shortcomings, and the globally optimal solution obtained did not appear to be plausible. In this article, an optimisation strategy is presented, which is able to find plausible and physically feasible solutions by detecting many local outcomes. The firefly algorithm mimics the natural behaviour of fireflies, which use a kind of flashing light to communicate with other members. An algorithm simulating the intensity of the light of a single firefly, diminishing with increasing distances, is implicitly able to detect local solutions on its way to the best solution in the search space. This implicit clustering feature is stressed by an additional explicit clustering step, where local solutions are stored and terminally processed to obtain a large number of possible solutions. The enhanced firefly algorithm will be first applied to the well-known Rastrigin functions and then to the tyre parametrisation problem. It is shown that the firefly algorithm is qualified to find a high number of optimisation solutions, which is required for plausible parametrisation for the given tyre model

Energy-Efficient Virtual Machine Placement using Enhanced Firefly Algorithm

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing

Enhancing security of MME handover via fractional programming and Firefly algorithm

Key update and residence management have been investigated as an effective solution to cope with desynchronisation attacks in Mobility Management Entity (MME) handovers. In this paper, we first analyse the impacts of the Key Update Interval (KUI) and MME Residence Interval (MRI) on handover processes and their secrecy performance in terms of the Number of Exposed Packets (NEP), Signaling Overhead Rate (SOR) and Outage Probability of Vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviours at the boundary of the KUI and MRI, but also show the trade-off between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchonisation attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a Multi- objective Optimisation (MO) problem to find the optimal KUI and MRI that minimise both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarisation techniques are adopted to transform the proposed MO problem into single- objective optimisation problems, i.e., an achievement-function method via Fractional Programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the Firefly Algorithm (FA) is utilised to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique

A Comprehensive Review of Recent Variants and Modifications of Firefly Algorithm

Swarm intelligence (SI) is an emerging field of biologically-inspired artificial intelligence based on the behavioral models of social insects such as ants, bees, wasps, termites etc. Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. In the last two decades, there has been a growing interest of addressing Dynamic Optimization Problems using SI algorithms due to their adaptation capabilities. This paper presents a broad review on two SI algorithms: 1) Firefly Algorithm (FA) 2) Flower Pollination Algorithm (FPA). FA is inspired from bioluminescence characteristic of fireflies. FPA is inspired from the the pollination behavior of flowering plants. This article aims to give a detailed analysis of different variants of FA and FPA developed by parameter adaptations, modification, hybridization as on date. This paper also addresses the applications of these algorithms in various fields. In addition, literatures found that most of the cases that used FA and FPA technique have outperformed compare to other metaheuristic algorithms

Biological System Behaviours and Natural-inspired Methods and Their Applications to Supply Chain Management

People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry