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The performance of two recent variants of Particle Swarm Optimization (PSO) when applied to Integer Programming problems is investigated. The two PSO variants, namely, barebones Particle Swarm (BB) and the exploiting barebones Particle Swarm (BBExp) are compared with the standard PSO and standard Differential Evolution (DE) on several Integer Programming test problems. The results show that the BBExp...
This paper presents several extensions to an algorithm in the family of Ant Colony Optimization, the Ant Colony System. The proposed extensions are based on the idea of opposition and attempt to increase the exploration efficiency of the solution space. The modifications focus on the solution construction phase of the ant colony system. Three of the proposed methods work by pairing the ants and synchronizing...
As the particle swarm paradigm matures, some bad habits are adopted and some good practices are ignored. This paper gives an informal discussion of some of issues and practices that may affect the course of future development of the algorithm.
An analytical framework is presented to study the self-adaptive behavior of probabilistic routing protocols for computer networks. Such soft routing protocols have attracted attention for delivering packets more reliably, robustly, and efficiently than conventional deterministic approaches. Efficient global operating parameters can be estimated without resorting to expensive Monte-Carlo simulation...
When applying Particle Swarm Optimization (PSO) to real world optimization problems, often boundary constraints have to be taken into account. In this paper, we will show that the bound handling mechanism essentially influences the swarm behavior, especially in high-dimensional search spaces. In our theoretical analysis, we will prove that all particles are initialized very close to the boundary with...
The 0-1 quadratic knapsack problem (QKP) is a hard computational problem, which is a generalization of the knapsack problem (KP). In this paper, a mini-Swarm system is presented. Each agent, realized with minor declarative knowledge and simple behavioral rules, searches on a structural landscape of the problem through the guided generate-and-test behavior under the law of socially biased individual...
In the 3-DOF(degree-of-freedom) flight simulator system, the relations between observed information and fault causes are very complicated. Based on the description of the basic principle of the ant colony algorithm, a novel hybrid approach for fault diagnosis in 3-DOF flight simulator is proposed in this paper, which is based on BP(back propagation) neural network and ant colony algorithm. Combining...
A new approach to prevent negative emergent behaviors of adaptive or organic computing systems is presented. One characteristic of such computing systems is the use self-organisation principles from nature and components that make decentralized decisions. To control such systems is a difficult task. In this paper we propose to control by introducing a swarm of so called anti-components to the system...
A new clustering technique by the use of multiple swarms is proposed. The proposed technique mimics the behavior of biological swarms which explore food situated in several places. We model the clustering problem using particle swarm optimization (PSO) approach. The proposed method considers multiple cooperating swarms to find centers of clusters. By assigning a portion of the solution space to each...
This paper presents an automatic tuning method of model predictive control (MPC) using particle swarm optimization (PSO). Although conventional PID is difficult to treat constraints and future plant dynamics, MPC can treat this issues and practical control can be realized in various industrial problems. One of the challenges in MPC is how control parameters can be tuned for various target plants and...
In this paper, the performance of some evolutionary algorithms on grinding process optimization of silicon carbide (SiC) is investigated. The grinding of SiC is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the Material Removal Rate (MRR) while maintaining...
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...
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...
Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm Optimization algorithm, which uses a virtual...
Micro robots in large scale swarms often have a very restricted program memory which limits the robot's application range. We present a finite state machine operating system for swarm micro robots, that can overcome such problems and gives the designer of swarm algorithms a tool that is easy to handle. The operating system's flow control or rather the robot's control program is represented in the...
A key problem of component-based grid application configuration is to map services onto the execution nodes of the grid environment such that all services of the application satisfy some minimum quality requirements. This problem is known to be NP-hard. This paper presents two extensions to our previous ant-based application service mapping heuristic, in order to establish some coordination among...
NASA is conducting research on advanced technologies for future exploration using intelligent swarms of robotic vehicles. One of these missions is the Autonomous Nano Technology Swarm (ANTS) mission that will explore the asteroid belt using 1,000 cooperative autonomous spacecraft. From an engineering point of view, the complexity and emergent behavior of this kind of system is one of the main challenges...
This paper presents a novel flocking strategy for a large-scale swarm of robots that enables the robots to navigate autonomously in an environment populated with obstacles. Robot swarms are often required to move toward a goal while adapting to changes in environmental conditions in many applications. Based on the observation of the swimming behavior of a school of tunas, we apply their unique patterns...
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...
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