Improving Learning Automata based Particle Swarm: An optimization algorithm

Optimization (PSO) algorithms have been recently developed, with the best aim of escaping from local minima. One of these recent variations is PSO-LA model which employs a Learning Automata (LA) that controls the velocity of the particle. Another variation of PSO enables particles to dynamically search through global and local space. This paper presents a Dynamic Global and Local Combined Particle Swarm Optimization based on a 3-action Learning Automata (DPSOLA). The embedded learning automaton accumulates the information from individuals, local best and global best particles then combines them to navigate the particle through the problem space. The proposed algorithm has been tested on eight benchmark functions with different dimensions. The work is unique from its test bed; evaluations contain large population size (150) and high dimension (150). The results show that, fitness and convergence pace is better than traditional PSO, DGLCPSO and previous PSO based LA algorithms. I

Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Generalised cellular neural networks (GCNNs) constructed using particle swarm optimisation for spatio-temporal evolutionary pattern identification

Particle swarm optimization (PSO) is introduced to implement a new constructive learning algorithm for training generalized cellular neural networks (GCNNs) for the identification of spatio-temporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This new algorithm will thus be referred to as the orthogonal projection pursuit (OPP) algorithm, which is in mechanism similar to the conventional projection pursuit approach. A novel two-stage hybrid training scheme is proposed for constructing a parsimonious GCNN model. In the first stage, the orthogonal projection pursuit algorithm is applied to adaptively and successively augment the network, where adjustable parameters of the associated units are optimized using a particle swarm optimizer. The resultant network model produced at the first stage may be redundant. In the second stage, a forward orthogonal regression (FOR) algorithm, aided by mutual information estimation, is applied to re. ne and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modeling framework to a spatio-temporal evolutionary system identification problem

Lenia and Expanded Universe

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment

Firefly Algorithm: Recent Advances and Applications

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.Comment: 15 page

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given