Overview of Algorithms for Swarm Intelligence (original) (raw)

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.

Swarm Intelligence: Past, Present and Future

Soft Computing, 2017

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

Swarm Intelligence: State of Art

2019

The paper explores Swarm Optimization and its application on different field. Here, an attempt has been made to achieve the inline relationship between the Swarm Optimization and Genetic Algorithm. We have worked to solve the problems related to optimization using those various algorithms. In this paper, we have provided solution to minimize the population of different colonies especially using cost functions.

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.

Swarm Intelligence an Inspiration from Social Insect Behavior in Various Decision Making Algorithms

A swarm is a large number of homologous, intelligible workers collaborating locally among themselves, and their environment, with no paramount control. The intelligence possessed by these swarms is known as swarm intelligence. Swarm Intelligence (SI) is a branch of Artificial Intelligence (AI) that is used to model the cumulative behavior of social swarms in nature, such as ant colonies, honey bees, and bird flocks. This paper focuses on the how the intelligence from the swarms has exhilarated for the development of various algorithms for solving hard problems and it also outlines two of the most acknowledged methods of optimization techniques motivated by swarm intelligence: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

Comparison of Swarm Intelligence Techniques

Swarm intelligence is a computational intelligence technique to solve complex real-world problems. It involves the study of collective behaviour of behavior of decentralized, self-organized systems, natural artificial. Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. In this paper, we have made extensive analysis of the most successful methods of optimization techniques inspired by Swarm Intelligence (SI): Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An analysis of these algorithms is carried out with fitness sharing, aiming to investigate whether this improves performance which can be implemented in the evolutionary algorithms.

Swarm Intelligence and Its Applications

The Scientific World Journal, 2013

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

Swarm intelligence: foundations, perspectives and applications

2006

Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged [14, 7, 22].

Editorial: Special Issue on Swarm Intelligence Algorithms and Applications

2011

Swarm intelligence (SI) is generally to study the collective behaviour in a decentralized system which is made up by a population of simple individuals interacting locally with one another and with their environment. Such systems are often be found in nature, including bird flocking, ant colonies, particles in cloud, fish schooling, bacteria foraging, animal herding, honey bees, spiders, and sharks, just to name a few.