Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning

Particle Swarm Optimization (PSO) is a population-based metaheuristic that can be applied to optimize controllers for multiple robots using only local information. In order to cope with noise in the robotic performance evaluations, different re-evaluation strategies were proposed in the past. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of distributed PSO in the presence of noise. In particular, we compare a distributed PSO OCBA algorithm suitable for resource-constrained mobile robots with a centralized version that uses global information for the allocation. We show that the distributed PSO OCBA outperforms a previous distributed noise-resistant PSO variant, and that the performance of the distributed PSO OCBA approaches that of the centralized one as the communication radius is increased. We also explore different parametrizations of the PSO OCBA algorithm, and show that the choice of parameter values differs from previous guidelines proposed for stand-alone OCBA

Task Allocation among Connected Devices: Requirements, Approaches and Challenges

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning

Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization problem. Their aim is to increase the robustness of the generated robotic controllers, as compared to previous PSO algorithms. We show that we can successfully apply this set of PSO algorithms to learn 166 parameters of a robotic controller for the obstacle avoidance task. We also study the impact that an increased evaluation budget has on the robustness and average performance of the optimized controllers. Finally, we validate the control solutions learned in simulation by testing the most robust controller in three different real arenas

Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots

The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots

Distributed Multi-Robot Learning using Particle Swarm Optimization

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive optimization problem. Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools to approach this problem. We focus this research on the Particle Swarm Optimization (PSO) algorithm, which, in addition to dealing with noise, allows a distributed implementation, speeding up the optimization process and adding robustness to failure of individual agents. In this thesis, we systematically analyze the different variables that affect the learning process for a multi-robot obstacle avoidance benchmark. These variables include algorithmic parameters, controller architecture, and learning and testing environments. The analysis is performed on experimental setups of increasing evaluation time and complexity: numerical benchmark functions, high-fidelity simulations, and experiments with real robots. Based on this analysis, we apply the distributed PSO framework to learn a more complex, collaborative task: flocking. This attempt to learn a collaborative task in a distributed manner on a large parameter space is, to our knowledge, the first of such kind. In addition, we address the problem of noisy performance evaluations encountered in these robotic tasks and present a %new distributed PSO algorithm for dealing with noise suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication

Bio-Inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Automated optimization of reconfigurable designs

Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.Open Acces