Study Of The Swarm Intelligence Algorithms (original) (raw)
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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.
A Literature Based Survey on Swarm Intelligence Inspired Optimization Technique
2015
Nature is of course a great and enormous source of inspiration for solving hard and composite problems in the field of computer science, transportation engineering, mechanical engineering, management and so on, since it exhibits exceptionally diverse, dynamic, robust, and complex phenomenon. It always finds the optimal solution to resolve its problem establishing perfect balance among its components. This is the driving force behind bio inspired computing. Nature inspired algorithms are meta heuristics that mimics the nature for solving optimization problems thus opening a new era in computation .For the past decades ,numerous research efforts has been concentrated in this particular area. Still being infantile and the results being very astonishing, broadens the scope and feasibility of Nature Inspired Algorithms (NIAs) exploring new areas of application and more opportunities in computing. This paper highlights the comparative analysis of nature inspired swarm Intelligence based o...
Analysis of Search Space in the Domain of Swarm Intelligence
Algorithms for Intelligent Systems, 2021
Bio-inspired algorithm which are replication of evolution and foraging pattern of different living entity exist on the world are broadly classified in two sub-field: Evolutionary and swarm-based algorithm. Evolutionary algorithms are derived from the theory of survive in nature by increased population, progress, companion, mate selection and breeding. Genetic algorithm, differential evolution, evolution strategy are few among them. Swarm algorithms are inspired from foraging process which exhibit social and cognitive behavior, decentralize and self-organized pattern of swarm. Particle swarm optimizer, artificial bee colony algorithm, glowworm swarm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, gray wolf optimizer, Spider Monkey Optimization are the algorithms following swarm approach.
Introductory Review of Swarm Intelligence Techniques
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.
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.
A comparative Evaluation of Swarm Intelligence Algorithm Optimization: A Review
Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI), 2021
Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. Optimization contributes to optimal resource management by way of efficient and effective problem-solving. Engineers' attention has been driven to more effective and scalable metaheuristic algorithms as a result of the complicated optimization issues. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. The authors see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores several algorithms such as ACO, PSO, GA, and FA.
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.
Overview of Algorithms for Swarm Intelligence
Computational Collective Intelligence. Technologies and Applications, 2011
Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.