A Survey to Nature Inspired Soft Computing

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...

Swarm Intelligence and Bio-Inspired Computation, 2013

Studies in computational intelligence, 2022

With the rapid upliftment of technology, there has emerged a dire need to 'fine-tune' or 'optimize' certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.

International Journal of Engineering Sciences & Research Technology, 2012

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.

Indian Journal of Science and Technology, 2016

Background /Objectives: In today's world, finding a feasible solution for combinatorial problems becoming a crucial task. The main objective of this paper is to analyze and comprehend different nature based algorithms enabling to find optimal solution. Methods/statistical analysis: Bacterial Foraging Algorithm (BFOA), firefly algorithm, Ant Colony Optimization (ACO), bee colony optimization, cuckoo optimization etc. Which have been used in power load balancing, cost estimating, optimal routing, color segmentation were discussed. This paper also highlights the constraints and convergence properties of each algorithm to solve certain problems encountered in various fields of application. Findings: Ant colony algorithms were successful in finding solutions within 1% of known optimal solutions. Optimal solution was found in BFOA by adjusting chemo taxis step size. Also, this paper analyzes results of various research works done in numerous fields using the swarm intelligence techniques.

In the era of digitalization, every task is performed with the help of software-dependent applications. Therefore, the developed software is required to be robust, reliable, and fault free. Testing is performed to check the functioning of the developed software to evaluate whether the software product is error-free or not. Test cases play a vital role in the testing process. However, with the advancement of time, a particular test suite becomes so lengthy that the execution of all the test cases is not possible due to limited time and resources. Researchers have proposed diverse techniques to make the testing process an effective one. This study has worked towards finding the usage of bio-inspired computing algorithms used for optimization. The reason behind this is because these algorithms have performed exceptionally well in addressing complex problems to provide workable solutions in a reasonable time. It is observed that only a handful of these algorithms were applied in testing, such as ant colony optimization, bee colony optimization, neural networks, and genetic algorithms. Even progress is made in the limited area of these algorithms. This study was conducted with a motive to sort out the most popular bio-inspired algorithms and to explore their working principles, developments made till now, along with the scope of their application. This paper has discussed how the development of these algorithms has progressed from already explored algorithms to the development of many new ones such as cuckoo search, artificial bee colony, bat algorithm, firefly algorithm, flower pollination algorithm, and many more. This study will help the researchers to gain insight into choosing the algorithm and explore them in developing new techniques for optimization.

Abstract─ Ant Algorithms are techniques for optimizing which were coined in the early 1990"s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. The focus of ant algorithms is to find approximate optimized problem solutions using artificial ants and their indirect decentralized communications using synthetic pheromones. In this paper, at first ant algorithms are described in details, then transforms to computational optimization techniques: the ACO metaheuristics and developed ACO algorithms. A comparative study of ant algorithms also carried out, followed by past and present trends in AAs applications. Future prospect in AAs also covered in this paper. Finally a comparison between AAs with well-established machine learning techniques were focused, so that combining with machine learning techniques hybrid, robust, novel algorithms could be produces for outstanding result in future.

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.