Abstract—The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm
Swarm is a multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of simulation is the swarm, a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling appr
最新PSO，最优化，粒子群最优化算法。Previously titled "Another Particle Swarm Toolbox" Introduction Particle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of large groups of simple animals
Key Features Unleash the power of operating Docker Swarm, Docker Machine and Docker Compose together. Get to grips with Docker Swarm, one of the key components of the Docker ecosystem A comprehensive guide that focuses on Swarm, a super-easy orchest
类似java的API文档： Action - interface swarm.activity.Action. An action type that has been customized for direct execution by an action interpreter.. ActionArgs - interface swarm.activity.ActionArgs. Supertype of ActionCall, ActionTo, and ActionForEach..
The proposed Grasshopper Optimisation Algorithm (GOA) mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems.
Chapter 1, Welcome to Docker Swarm, introduces Swarm, and explains why you need a clustering solution for your containers. It illustrates the Swarm features, giving a high-level descr iption of its architecture. We define some use cases and describe
Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research
This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group i
Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions (Pragmatic Programmers) by Frances Buontempo English, 2019,243 Pages Self-driving cars, natural language recognition, and online recommendation engines ar
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and c
This paper considers a class of social foraging swarms with a nutrient profile (or an attractant/repellent) and an attraction-repulsion coupling function, which is chosen to guarantee collision avoidance between individuals. The paper also studies no
