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This paper introduces the HelixPSO Particle Swarm Optimization (PSO) algorithm for finding minimum energy RNA secondary structures. It is shown experimentally that HelixPSO profits when it is combined with a genetic algorithm that finds a good starting population for HelixPSO. On all test instances this hybrid variant of HelixPSO performs significantly better than a state-of-the-art genetic algorithm...
A recurrent neural network (RNN) trained with a combination of particle swarm optimization (PSO) and backpropagation (BP) algorithms is proposed in this paper. The network is used as a dynamic system modeling tool to identify the frequency-dependent impedances of power electronic systems such as rectifiers, inverters, and DC-DC converters. As a category of supervised learning methods, the various...
Fuzzy Cognitive Maps constitute an important simulation methodology that combines neural networks and fuzzy logic. The Fuzzy Cognitive Maps designed by the experts can be enhanced significantly through learning algorithms, which proved to increase their efficiency and accuracy of simulation. Recently, learning algorithms that employ Particle Swarm Optimization for the minimization of properly defined...
In this paper, a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm based on particle swarm optimization (PSO) is proposed for multiple base stations under general power-consumption constraints. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes...
This paper presents the concept as well as first results of the EU-MOP ("Elimination Units for Marine Oil Pollutions") project1. The basic idea of this project is a swarm out of autonomous marine robots which are able to recover oil with the help of oil skimmers. In order to achieve a flexible and robust system, the swarm intelligence (SI) approach has been used as control paradigm for the...
The pheromone trail laying and trail following behaviors of ants have proved to be an efficient mechanism to optimize path selection in natural as well as in artificial networks. Despite this efficiency, this mechanism is under-used in collective robotics because of the chemical nature of pheromones. In this paper we present a new experimental setup which allows to investigate with real robots the...
In swarm robotic systems emergent swarm properties are particularly difficult to analyse and model. This paper describes a simple but effective algorithm for emergent swarm taxis (swarm motion toward a beacon) in a 2D or 3D wireless connected swarm of minimalist mobile robots. The paper then undertakes a deep analysis of the swarm taxis by identifying both first and second order micro-level robot...
This paper describes the experimental results of using the Particle Swarm Optimization (PSO) algorithm to control a suite of robots. In our approach, each bot is one particle in the PSO; each particle/bot makes measurements, updates its own position and velocity, updates its own personal best measurement (pbest) and personal best location (if necessary), and broadcasts to the other bots if it has...
The sequential ordering problem is a version of the asymmetric traveling salesman problem where precedence constraints on vertices are imposed. A tour is feasible if these constraints are respected, and the objective is to find a feasible solution with minimum cost. The sequential ordering problem models a lot of real world applications, mainly in the fields of transportation and production planning...
Penalty methods are often used to handle constraints in optimization problems. However, to find the optimal or near optimal set of penalty parameters is a hard task. Also, such values are problem dependent. This paper introduces the stochastic ranking approach to balance objective and penalty functions stochastically in a rank-based ACO metaheuristic. The results presented show that the simple inclusion...
This paper presents three ant colony optimization (ACO) approaches for a difficult graph theoretic problem formulated from the task of computing load-balanced clusters in ad hoc networks. These three approaches contain novel strategies for adapting the search process to the new problem structure whenever an environment change occurs. An environment change occurs when nodes in the network move. Dynamic...
In this paper, an algorithm based on Ant Colony Optimization metaheuristic is proposed for finding solutions to the well-known graceful labeling problem of graphs. Despite the large number of papers published on the theory of this problem, there are few particular techniques introduced by researchers for gracefully labeling graphs. The proposed algorithm is applied to many classes of graphs, and the...
Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm, and has been successfully applied to many different kinds of problems. But it is still an open problem that why PSO can be successful. Most of current PSO studies are empirical, with only a few theoretical analyses, and these theoretical studies concentrate mainly on simplified PSO systems, discarding...
Particle Swarm Optimization (PSO) is proposed as an efficient algorithm for simulation of high speed interconnects used in today's digital applications. First, a generic methodology is proposed for high speed interconnects simulation using PSO and finally comparisons are made between the performance of PSO compared to traditional optimization techniques used in high-speed serial bus simulation.
This paper outlines an approximate algorithm for finding an optimal decentralized control in multi-agent systems. Decentralized Partially Observable Markov Decision Processes and their extension to infinite state, observation and action spaces are utilized as a theoretical framework. In the presented algorithm, policies of each agent are represented by a feedforward neural network. Then, a search...
A new, almost parameter-free optimization algorithm is developed in this paper as a hybrid of the barebones particle swarm optimizer (PSO) and differential evolution (DE). The DE is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. Results of this algorithm are compared to that of the barebones...
Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward...
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