ENGLISH EDITION A Brief Review of Nature-Inspired Algorithms for Optimization (original) (raw)
Related papers
Studies in Computational Intelligence, 2021
This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
Nature-inspired metaheuristic algorithms
Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.
Nature-Inspired Optimization Algorithms: Research Direction and Survey
ArXiv, 2021
Nature-inspired algorithms are commonly used for solving the various optimization problems. In past few decades, various researchers have proposed a large number of nature-inspired algorithms. Some of these algorithms have proved to be very efficient as compared to other classical optimization methods. A young researcher attempting to undertake or solve a problem using nature-inspired algorithms is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some score over others. In this paper, an attempt has been made to summarize various leading research proposals that shall pave way for any new entrant to easily understand the journey so far. Here, we classify the nature-inspired algorithms as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, widely acknowledged nature-inspired algorithms namely- ACO, ABC, EAM, FA, FPA, GA, GSA, JAYA, PSO, SFLA, TLBO and WCA, have been studied. The purpose of this review is to present an exhaustive analysis of various nature-inspired algorithms based on its source of inspiration, basic operators, control parameters, features, variants and area of application where these algorithms have been successfully applied. It shall also assist in identifying and short listing the methodologies that are best suited for the problem.
A Review of Nature-Inspired Algorithms
Journal of Bionic Engineering, 2010
The study of bionics bridges the functions, biological structures and organizational principles found in nature with our modern technologies, and numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various computation methods that can be applied in different areas. People have learnt from biological systems 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 this paper, a number of selected nature-inspired algorithms are systematically reviewed and analyzed. Though the paper is mainly focused on the original principle behind each of the algorithm, their applications are also discussed.
Study : Evolution of Nature Inspired Algorithms in Various Application Domains
2017
Nature is the best guide and its outlines and qualities are to a great degree monstrous and abnormal that it offers motivation to looks into to impersonate nature to take care of hard and complex issues in computer sciences. Bio Inspired figuring has come up as a new period in calculation covering extensive variety of uses. The Nature inspired algorithm are in hype with more impactful results in various application domain. This paper consist of detailed study about the recent advances in nature inspired optimization methods. This paper also gives the flash light over the various optimization algorithm with its aim. Moreover, it includes the comparative study between the Swarm intelligence algorithms. It also discusses the applicability of various algorithm. These kind of nature-inspired algorithms are used widely in various fields for solving a variety of problems like travelling agent problem, in bio-information, in scheduling, clustering and mining problems, image processing, engi...
Nature inspired optimization algorithms or simply variations of metaheuristics?
Artificial Intelligence Review, 2020
In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing natureinspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new natureinspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms. Keywords Nature-inspired intelligent (NII) algorithms • Guidelines for nature-inspired algorithms • AI and optimization • Evaluation of algorithm's innovation * Alexandros Tzanetos
A Survey : Evolutionary and Swarm Based Bio-Inspired Optimization Algorithms
2012
Nature is the best tutor and its designs and strengths are extremely massive and strange that it gives inspiration to researches to imitate nature to solve hard and complex problems in computer sciences. Bio Inspired computing has come up as a new era in computation covering wide range of applications. This paper gives overview of most predominant and successful classes of bio inspired optimization methods involving evolutionary and swarm based algorithms inspired by natural evolution and collective behavior in animals respectively.
Survey on five nature-Inspired Optimization Algorithms
Gradus, 2021
This paper presents a literature review about Particle Swarm Optimization (PSO), Firework, Firefly, Clonal Selection, and Cuckoo Search algorithms, which are among the most common natural-inspired optimization algorithms. These algorithms were tried on different benchmark functions. The obtained results were analyzed, and the performance was compared. The results showed that PSO and Firefly Search algorithms provided the best performance in the studied cases.
An Exhaustive Survey on Nature Inspired Optimization Algorithms
International Journal of Digital Contents and Applications for Smart Devices, 2014
Human being are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solution to these problems. Nature acts as an optimizer for solving the complex problems. In this paper, the algorithms which are discussed imitate the processes running in nature. And due to this these process are named as "Nature Inspired Algorithms". The algorithms inspired from human body and its working and the algorithms inspired from the working of groups of social agents like ants, bees, and insects are the two classes of solving such Problems. This emerging new era is highly unexplored young for the research. This paper proposes the high scope for the development of new, better and efficient techniques and application in this area.