Swarm intelligence: foundations, perspectives and applications

Nature has always been a rich inspirational source over the ages, with much still to learn from and discover about. Swarm Intelligence (SI) is a relatively new and potentially promising branch of Artificial Intelligence that is used to model the collective intelligent behavior of social swarms in nature. This technical report provides a general introduction to SI, and offers a fundamental overview of two of the most popular SI paradigms: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Our main objective is to help readers with no prior familiarity in SI to gain high-level understanding of its principles, advantages and limitations, and to increase the level of familiarity and interest in such a fastgrowing and far-reaching research area. This technical report presents a critical discussion of ACO and PSO, and compares them with two of the most popular machine learning algorithms: Artificial Neural Networks (ANN) and Genetic Algorithms (GAs).

Swarm Intelligence (SI) describes the evolving collective intelligence of population/groups of autonomous agents with a low level of intelligence. Particle Swarm Optimization (PSO) is an evolutionary algorithm inspired by animal social behaviour. PSO achieves performance by iteratively directing its particles toward the optimum using its social and cognitive components. Various modifications have been applied to PSO focusing on addressing a variety of methods for adjusting PSO's parameters (i.e., parameter adjustment), social interaction of the particles (i.e., neighbourhood topology) and ways to address the search objectives (i.e., sub-swarm topology). The PSO approach can easily fit in different search optimization categories such as Self Learning, Unsupervised Learning, Stochastic Search, Population-based, and Behaviour-based Search. This study addresses these principal aspects of PSO. In addition, conventional and Basic PSO are introduced and their shortcomings are discussed. Later on, various suggestions and modifications proposed by literature are introduced and discussed.

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. Research in SI started in the late 1980s. Besides the applications to conventional optimization problems, SI can be employed in library materials acquisition, communications, medical dataset classification, dynamic control, heating system planning, moving objects tracking, and prediction. Indeed, SI can be applied to a variety of fields in fundamental research, engineering, industries, and social sciences. The main objective of this special issue is to provide the readers with a collection of high quality research articles that address the broad challenges in application aspects of swarm intelligence and reflect the emerging trends in state-of-the-art algorithms. The special issue received 42 high quality submissions from different countries all over the world. All submitted papers followed the same standard (peer-reviewed by at least three independent reviewers) as applied to regular submissions to "this journal". Due to the limited space, 15 papers

Particle swarm optimization is a heuristic global optimization method put forward originally by Doctor Kennedy and Eberhart in 1995. Various efforts have been made for solving unimodal and multimodal problems as well as two dimensional to multidimensional problems. Efforts were put towards topology of communication, parameter adjustment, initial distribution of particles and efficient problem solving capabilities. Here we presented detail study of PSO and limitation in present work. Based on the limitation we proposed future direction. I. INTRODUCTION 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. PSO is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems, or the problems that can be transformed to function optimization problems. As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA) and other global optimization algorithms. While population-based heuristics are more costly because of their dependency directly upon function values rather than derivative information, they are however susceptible to premature convergence, which is especially the case when there are many decision variables or dimensions to be optimized. Particle swarm optimization is a heuristic global optimization method put forward originally by Doctor Kennedy and Eberhart in 1995. While searching for food, the birds are either scattered or go together before they locate the place where they can find the food. While the birds are searching for food from one place to another, there is always a bird that can smell the food very well, that is, the bird is perceptible of the place where the food can be found, having the better food resource information. Because they are transmitting the information, especially the good information at any time while searching the food from one place to another, conduced by the good information, the birds will eventually flock to the place where food can be found. As far as particle swam optimization algorithm is concerned, solution swam is compared to the bird swarm, the birds' moving from one place to another is equal to the development of the solution swarm, good information is equal to the most optimist solution, and the food resource is equal to the most optimist solution during the whole course. The most optimist solution can be worked out in particle swarm optimization algorithm by the cooperation of each individual. The particle without quality and volume serves as each individual, and the simple behavioral pattern is regulated for each particle to show the complexity of the whole particle swarm. In PSO, the potential solution called particles fly through the problem space by following the current optimum particles. Each particles keeps tracks of its coordinates in the problem space which are associated with the best solution (fitness) achieved so far. This value is called as pbest. Another best value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This value is called lbest. When a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest (for lbest version). Acceleration is weighted by random term, with separate random numbers being generated for acceleration towards pbest and lbest locations. After finding the best values, the particle updates its velocity and positions with following equations.

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.

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).

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.

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.