Nature-Inspired Optimization Algorithms (original) (raw)

ENGLISH EDITION A Brief Review of Nature-Inspired Algorithms for Optimization

2016

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very efficient and thus have become popular tools for solving real-world problems. Some algorithms are insufficiently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

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.

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.

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

Nature-Inspired Algorithms in Real-World Optimization Problems

MENDEL, 2017

Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.

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.

A Comparative Study of a Class of Nature Inspired Algorithms

bvicam.ac.in

Evolutionary Computation (EC) is a vibrant area of investigation which has been enormously successful with more than hundreds of publications within a decade. Some of the widely known approaches being Ant Colony Optimization, Particle Swarm Optimization, Bees Algorithm and Genetic Algorithm. All of these can be used to solve a variety of problems. In fact there are so many papers, that it is sometimes difficult to understand the pros, cons and applicability of each one of them. This paper makes an attempt to compare the basic idea, applicability, limitations and effectiveness of the four effective approaches.

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...