In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuz...

Swarm intelligence (SI) is widely used in many complex optimization problems. It is a collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This paper presents a detailed overview of Particle Swarm Optimization (PSO), its variants and hybridization of PSO with Bee Algorithm (BA). This paper also surveys various SI techniques p...

A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for m...

In this paper, we propose three specific scenarios in order to allow one to analyze the performance of swarmintelligence based coordination models for swarm of robots. The specific scenarios aim to assess some features presented on swarm robots: (i) contraction and expansion; (ii) selfsegregation and self-aggregation; and (iii) the capacity to change abruptly the fly direction whenever it is ne...

This paper presents particle swarm optimization (PSO) method to find the prime factors of a composite number. Integer factorization is a well known NP hard problem and security of many cryptosystem is based on difficulty of integer factorization. A particle swarm optimization algorithm for integer factorization has been devised and tested on different 100 numbers. It has been found that the PSO...

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in burs...

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. The paper introduces the evaluation index of particle swarm premature convergence to judge the state of particle swarm in the population space, for the sake o...

This proof of concept study examines the possibility of specifying the construction of programs using a Particle Swarm algorithm, and represents a new form of automatic programming based on Social Learning, Social Programming or Swarm Programming. Each individual particle represents choices of program construction rules, where these rules are specified using a Backus-Naur Form grammar. The resu...

This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called particle swarm optimization with mutation operators (PSOM). The mutation operators are ...