In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Moth-flame optimization algorithm

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

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Biomechanics And Motor Control Of Human Movement

Solar and Terrestrial Radiation

Symbiotic Organisms Search: A new metaheuristic optimization algorithm

Publisher Summary This chapter presents a new population-based search algorithm called the Bees Algorithm (BA). This algorithm mimics the food foraging behavior of swarms of honeybees. In its basic version, the algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization. This chapter focuses on the latter. Following a description of the algorithm, the chapter presents the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm. The results show that the algorithm can reliably handle complex multi-model optimization problems without being trapped at local solutions. One of the drawbacks of the algorithm is the number of tunable parameters used. However, it is possible to set the parameter values by conducting a small number of trials.

/pdf/the-bees-algorithm-a-novel-tool-for-complex-optimisation-4i0xyuab22.pdf

The bees algorithm, a novel tool for complex optimisation problems

https://rms.scu.ac.ir/Files/Articles/Journals/Abstract/solar.pdf2010116201834625.pdf

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

http://rms.scu.ac.ir/Files/Articles/Journals/Abstract/Application%20of%20PSO%20(particle%20swarm%20optimization)%20and%20GA%20(genetic%20algorithm)%20techniques%20on%20demand%20estimation%20of%20oil%20in%20Iran.pdf201141893550203.pdf

Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

This paper presents an application of the bees algorithm (BA) to the optimisation of neural networks for wood defect detection. This novel population-based search algorithm mimics the natural foraging behaviour of swarms of bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search. Following a brief description of the algorithm, the paper gives the results obtained for the wood defect identification problem demonstrating the efficiency and robustness of the new algorithm.

Optimising Neural Networks for Identification of Wood Defects Using the Bees Algorithm

Control charts are employed in manufacturing industry for statistical process control (SPC). It is possible to detect incipient problems and prevent a process from going out of control by identifying the type of patterns displayed by the control charts. Various techniques have been applied to this control chart pattern recognition task. This paper presents the use of Learning Vector Quantisation (LVQ) networks for recognising patterns in control charts. The LVQ networks were trained, not by applying standard training algorithms, but by employing a new optimisation algorithm developed by the authors. The algorithm, called the Bees Algorithm, is inspired by the food foraging behaviour of honey bees. The paper first describes the Bees Algorithm and explains how the algorithm is employed to train LVQ networks. It then discusses the recognition of control chart patterns by LVQ networks optimised using the Bees Algorithm.

Afshin Ghanbarzadeh

Papers

The bees algorithm, a novel tool for complex optimisation problems

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

Optimising Neural Networks for Identification of Wood Defects Using the Bees Algorithm

Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition