The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

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 higherdimensional optimisation problems.

Firefly algorithm: recent advances and applications

A survey on new generation metaheuristic algorithms

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

This paper addresses how to apply firefly algorithm (FA) for travelling salesman problem (TSP). Two schemes are studied, i.e. discrete distance between two fireflies and the movement scheme. Computer simulation shows that the simple form of FA without combination with other methods performs very well to solve some TSP instances, but it can be trapped into local optimum solutions for some other instances.

Evolutionary discrete firefly algorithm for travelling salesman problem

Algoritma Genetika merupakan salah satu teknik komputasi yang sangat sesuai untuk permasalahan dengan ruang solusi yang sangat besar. Kelebihan utama Algoritma Genetika adalah sifatnya adaptivitasnya. Kelemahan Algoritma Genetika adalah diperlukannya usaha yang cukup melelahkan untuk dapat menemukan nilai-nilai parameter Algoritma Genetika yang optimal.

Algoritma genetika dalam MATLAB

The “firefly algorithm” (FA) is a nature-inspired technique originally designed for solving continuous optimization problems. There are several existing approaches that apply FA also as a basis for solving discrete optimization problems, in particular the “traveling salesman problem” (TSP). In this chapter, we present a new movement scheme called edge-based movement, an operation which guarantees that a candidate solution more closely resembles another one. This leads to a more FA-like behavior of the algorithm. We investigate the performance of the ‘evolutionary discrete firefly algorithm” when using this new edge-based movement and compare it against previous methods. Computer simulations show that the new movement scheme produces slightly better accuracy with much faster average time. The average speedup factor is 14.06 times.

Discrete Firefly Algorithm for Traveling Salesman Problem: A New Movement Scheme

This paper describes an Informed Genetic Algorithm (IGA), a genetic algorithm using greedy initialization and directed mutation, to solve a practical university course and student timetabling problem. A greedy method creates some feasible solutions, where all specified hard constraints are not broken, as initial population. A directed mutation scheme is used to reduce violations regarding all given soft constraints and to keep the solutions feasible. Here, IGA creates a timetable in two stages. Firstly, IGA evolves a course timetable using any constraints regarding lecturer, class and room. This stage produce best-so-far timetable. Secondly, using some certain rules IGA evolves the best-so-far timetable using all constraints. The batch student sectioning is done by allowing the first stage timetable to change. Computer simulation to a highly constrained timetabling problem shows that the informed GA is capable of producing a reliable timetable.

An Informed Genetic Algorithm for University Course and Student Timetabling Problems

This paper describes a tool, called as automatic speech splitter (ASS) used in developing speech corpus. Two problems in developing a huge speech corpus are recording process and file renaming. Recording one by one sentence and storing the resulting speech in a file name are time consuming. Solutions to those problems are record n sentences simultaneously with a certain delay between two sentences, split the results into n speeches automatically using ASS, and then store the n speeches into n desired file names. Basic notion of ASS is classifying a speech input into silence and speech segments according to its short-term signal energy rate (SER). ASS successfully split a long speech containing n sentences into exactly n one-sentence-speeches with maximum error of 50 ms, but this error is acceptable since the tolerance is 100 ms.

Suyanto

Papers

Evolutionary discrete firefly algorithm for travelling salesman problem

Algoritma genetika dalam MATLAB

Discrete Firefly Algorithm for Traveling Salesman Problem: A New Movement Scheme

An Informed Genetic Algorithm for University Course and Student Timetabling Problems

Signal energy-based automatic speech splitter: a tool for developing speech corpus