Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Design of the Square Loop Frequency Selective Surfaces with Particle Swarm Optimization via the Equivalent Circuit Model

In this study, Particle Swarm Optimization is applied for the design of Square Loop Frequency Selective Surfaces (the conventional Square Loop, Gridded Square Loop, and Double Square Loop) via their equivalent circuits. For this purpose, first the derivation of the equivalent circuit formulation is revisited. Then an objective function, which is based on the transmission coefficients at various frequencies at the pass/stop-bands, is defined. By means of an ANSI C++ implementation, a platform independent console application (which depends on the Equivalent Circuit Models and continuous form of Particle Swarm Optimization) is developed. The obtained results are compared to those in the literature. It is observed that the Particle Swarm Optimization is perfectly suitable for this sort of problems, and the solution accuracy is limited to and dominated by that of the Equivalent Circuit Model

Swarm-based Algorithms for Neural Network Training

The main focus of this thesis is to compare the ability of various swarm intelligence algorithms when applied to the training of artificial neural networks. In order to compare the performance of the selected swarm intelligence algorithms both classification and regression datasets were chosen from the UCI Machine Learning repository. Swarm intelligence algorithms are compared in terms of training loss, training accuracy, testing loss, testing accuracy, hidden unit saturation, and overfitting. Our observations showed that Particle Swarm Optimization (PSO) was the best performing algorithm in terms of Training loss and Training accuracy. However, it was also found that the performance of PSO dropped considerably when examining the testing loss and testing accuracy results. For the classification problems, it was found that firefly algorithm, ant colony optimization, and fish school search outperformed PSO for testing loss and testing accuracy. It was also observed that ant colony optimization was the algorithm that performed the best in terms of hidden unit saturation

Inverse Design of Three-Dimensional Frequency Selective Structures and Metamaterials using Multi-Objective Lazy Ant Colony Optimization

With the rise of big data and the “internet of things,” wireless signals permeate today’s environment more than ever before. As the demand for information and security continues to expand, the need for filtering a crowded signal space will become increasingly important. Although existing devices can achieve this with additional components, such as in-line filters and low noise amplifiers, these approaches introduce additional bulk, cost and complexity. An alternative, low-cost solution to filtering these signals can be achieved through the use of Frequency Selective Surfaces (FSSs), which are commonly used in antennas, polarizers, radomes, and intelligent architecture. FSSs typically consist of a doubly-periodic array of unit cells, which acts as a spatial electromagnetic filter that selectively rejects or transmits electromagnetic waves, based on the unit cell’s geometry and material properties. Unlike traditional analog filters, spatial filters must also account for the polarization and incidence angle of signals; thus, an ideal FSS maintains a given frequency response for all polarizations and incidence angles. Traditional FSS designs have ranged from planar structures with canonical shapes to miniaturized and multi-layer designs using fractals and other space-filling geometries. More recently, FSS research has expanded into three-dimensional (3D) designs, which have demonstrated enhanced fields of view over traditional planar and multi-layer designs. To date, nearly all FSSs still suffer from significant shifts in resonant frequencies or onset of grating lobes at incidence angles beyond 60 degrees in one or more polarizations. Additionally, while recent advances in additive manufacturing techniques have made fully 3D FSS designs increasingly popular, design tools to exploit these fabrication methods to develop FSSs with ultra-wide Fields of View (FOV) do not currently exist. In this dissertation, a Multi-Objective Lazy Ant Colony Optimization (MOLACO) scheme will be introduced and applied to the problem of 3D FSS design for extreme FOVs. The versatility of this algorithm will further be demonstrated through application to the design of meander line antennas, optical antennas, and phase-gradient metasurfaces

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Natural Computing and Beyond

This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains