Алгоритмы роевого интеллекта реферат
Обновлено: 05.07.2024
The article results analysis algorithms Swarm intelligence as a special class of optimization algorithms. It shows fundamental differences between Swarm Intelligence algorithms and other stochastic optimization algorithms. UML-diagrams illustrating the unified approach proposed by author to swarm intelligence algorithms are given. Swarm Intelligence algorithms should be considered as a special group among population-based optimization algorithms, since they share the same characteristic idea, based on the collective movements of decentralized agents and the indirect information exchange. This distinguishes Swarm Intelligence of evolutionary algorithms that mimics the process of natural selection. Proposed unified description of Swarm Intelligence algorithms in case of their implementation provides standardization, enhance flexibility and portability of software and increasing the speed of development. The scheme is applied to describe the Artificial Bee Colony Optimization and the Fish School Search. Analogous descriptions of the Particle Swarm Optimization and Ant Colony Optimization have been given in this journal, no. 12, 2013.
Discover the world's research
- 20+ million members
- 135+ million publications
- 700k+ research projects
. В работе [8] приведена универсальная схема описания алгоритмов роевого интеллекта. По ней алгоритм светлячков следует записать в виде FF =
Optimizing of electric power systems is one of the main directions of research in the energy engineering. At present, methods other than classical optimization methods are based on various bio-heuristic algorithms. The problems of optimization of reactive power in a power grid using bio-heuristic algorithms are considered. These algorithms allow to obtain more efficient solutions and to take into account several criteria. The Fire Fly algorithm is adapted to optimize the placement of reactive power sources and to select their values. A key feature of the proposed modification of the firefly algorithm is the solution of the multi-objective optimization problem. Algorithms based on the bio-heuristic process can find a neighborhood of the global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, firefly algorithm and firefly algorithm with gradient descent is carried out.
Учебное пособие раскрывает принципы, модели и методы стохастической оптимизации, рекомендации по их реализации и применению на практике, дает примеры использования. Кроме того, пособие содержит описание практической работы по данной теме. Адресовано студентам и специалистам, изучающим методы оптимизации и системы искусственного интеллекта.
The article considers swarm intelligence algorithms from the position of system approach. It gives definitions to basic concepts of algorithms in terms of systems theory and suggests a unified scheme of description for all swarm intelligence algorithms. The scheme is applied to describe the Particle Swarm Optimization and Ant Colony Optimization.
A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial optimisation and functional optimisation. This paper focuses on the latter. Following a description of the algorithm, the paper gives the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm.
Swarm robotics is a novel approach to the coordination of large numbers of relatively simple robots which takes its inspiration from social insects. This paper proposes a definition to this newly emerging approach by 1) describing the desirable properties of swarm robotic systems, as observed in the system-level functioning of social insects, 2) proposing a definition for the term swarm robotics, and putting forward a set of criteria that can be used to distinguish swarm robotics research from other multi-robot studies, 3) providing a review of some studies which can act as sources of inspiration, and a list of promising domains for the utilization of swarm robotic systems.
This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.
What is an algorithm? An algorithm is a procedure to accomplish a specific task. An algorithm is the idea behind any reasonable computer program. To be interesting, an algorithm must solve a general, well-specifiedem problem. An algorithmic problem is specified by describing the complete set of instances it must work on and of its output after running on one of these instances. This distinction, between a problem and an instance of a problem, is fundamental.
The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity.
Cellular Robotic Systems are capable of ’intelligent* behavior. The meaning of this intelligence is analyzed in the paper. We define robot intelligence and robot system intelligence in terms of unpredictability of improbable behavior. The concept of unpredictability is analyzed in relation to (1) statistical unpredictability, (2) inaccessibility, (3) undecidability, (4) intractability, and (5) non-representability. We argue that the latter two type of unpredictability, when exhibited by systems capable of producing order, can result in a non-trivial, different form of intelligent behavior (Swarm Intelligence). Engineering problems related to Swarm Intelligence are mentioned in relation to Cellular Robotic Systems which consist of collections of autonomous, non-synchronized, non-intelligent robots cooperating to achieve global tasks.
Recommendations
Global optimization and Topological methods with applications in Materials science
Prediction of stable atomistic structure of bimetallic and other nanosystems. Quantitative parameters studying of the molecular graphs.
Swarm Intelligence for Scheduling: a Review
Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces . [Show full abstract] some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization.
Designing Fuzzy Rule Base using Spider Monkey Optimization Algorithm in Cooperative Framework
The paper focusses on the implementation of cooperative Spider Monkey Optimization Algorithm (SMO) to design and optimize the fuzzy rule base. Spider Monkey Optimization Algorithm is a fission-fusion based Swarm Intelligence algorithm. Cooperative Spider Monkey Algorithm is an off-line algorithm used to optimize all the free parameters in a fuzzy rule base. The Spider Monkeys are divided into . [Show full abstract] various groups the solution from each group represents a fuzzy rule. These groups work in a cooperative way to design the whole fuzzy rule base. Simulation on fuzzy rules of two nonlinear controllers is done with a parametric study to verify the performance of the algorithm. It is observed that the root mean square error (RMSE) is least in the case of SMO than the other evolutionary algorithms applied in the literature to solve the problem of fuzzy rule designs like Particle Swarm Optimization (PSO), Ant Colony Optimization algorithm (ACO) algorithms.
Genetical Swarm Optimization: a New Hybrid Evolutionary Algorithm for Electromagnetic Applications
In this paper a new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) will be presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the . [Show full abstract] particle swarm optimization (PSO) and genetic algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The algorithm is tested here with respect to the other optimization techniques dealing with two typical problems, a purely mathematical one, the search for the global maximum of a multi-dimensional sine function and an electromagnetic application, the optimization of a linear array
Improving Evolutionary Algorithm Performance for Feature Selection in High-Dimensional Data
In classification and clustering problems, selecting a subset of discriminative features is a challenging problem, especially when hundreds or thousands of features are involved. In this framework, Evolutionary Computation (EC) techniques have received a growing scientific interest in the last years, because they are able to explore large search spaces without requiring any a priori knowledge or . [Show full abstract] assumption on the considered domain. Following this line of thought, we developed a novel strategy to improve the performance of EC-based algorithms for feature selection. The proposed strategy requires to rank the whole set of available features according to a univariate evaluation function; then the search space represented by the first M ranked features is searched using an evolutionary algorithm for finding feature subsets with high discriminative power. Results of comparisons demonstrated the effectiveness of the proposed approach in improving the performance obtainable with three effective and widely used EC-based algorithm for feature selection in high dimensional data problems, namely Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC).
IAPSO-TCI: Improved Ant and Particle Swarm based Optimization Techniques for Classifying Imagery
The biologically inspired world comprising of social insect metaphor for solving out wide range of dilemma has become potentially promising area in most recent duration focusing on indirect or direct coordination's among diverse artificial agents. Swarm [8] apparently is a disorganized collection / population of moving individual that tends to cluster together while each individual seems to be . [Show full abstract] moving in random directions. Swarm Intelligence techniques include Particle swarm optimization, Ant Code Optimization, Biogeography based optimization, Bee Colony Optimization, Stochastic Diffusion Search, Bacterial foraging optimization. Classification is the computational procedure [1] [3] that arrange the images into groups according to their similarities. Plentiful methods for classification have been designed and investigating novel means to increase classification exactness has been a key topic. Ant Colony Optimization (ACO) [6] [11] [18] is an algorithm motivated by the foraging behaviour of ants wherein ants leaves the volatile substance called pheromone on the soil surface for the purpose of collective contact via indirect communications. Particle Swarm Optimization is an approach to problems whose solutions can be represented as a point in an n-dimensional solution space wherein number of particles [13] [19] are randomly set into motion through this space. In each of the iteration, they observe the "fitness" of themselves and their neighbours and "emulate" successful neighbours by moving towards them. This paper focuses on improved Methodology of Swarm Computing for classifying imagery termed as IAPSO-TCI exploring Improved Ant and Particle Swarm based Optimization using a traditional classifier SVM (Support Vector Machines) for edge detection and image classification
Читайте также: