Read e-book online Agent-based modeling and simulation with Swarm PDF

By Hitoshi Iba

ISBN-10: 146656234X

ISBN-13: 9781466562349

Swarm-based multi-agent simulation results in higher modeling of projects in biology, engineering, economics, paintings, and plenty of different parts. It additionally enables an realizing of advanced phenomena that can not be solved analytically. Agent-Based Modeling and Simulation with Swarm presents the technique for a multi-agent-based modeling technique that integrates computational ideas resembling man made lifestyles, mobile automata, and bio-inspired optimization.

Each bankruptcy provides an outline of the matter, explores cutting-edge expertise within the box, and discusses multi-agent frameworks. the writer describes step-by-step tips on how to gather algorithms for producing a simulation version, software, approach for visualisation, and extra examine projects. whereas the publication employs the widely used Swarm approach, readers can version and enhance the simulations with their very own simulator. To inspire hands-on exploration of emergent platforms, Swarm-based software program and resource codes can be found for obtain from the author’s web site.

A thorough evaluation of multi-agent simulation and assisting instruments, this ebook exhibits how this kind of simulation is used to procure an figuring out of complicated structures and synthetic lifestyles. It conscientiously explains the best way to build a simulation application for varied applications.

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There are a number of crossover methods based on the level of granularity in separating each individual, for example, the one-point crossover and the uniform crossover. The crossover shown in Fig. 2: GA operators. one-point crossover. Following are some methods for performing the crossover operation. 1. One-point crossover 2. Multi-point crossover (n-point crossover) 3. Uniform crossover We have already explained the one-point crossover operation (Fig. 3(a)). The n-point crossover operation has n crossover points, so if n = 1, this is equivalent to the one-point crossover operation.

In all methods, individuals who have higher fitness are more likely to be selected. • Roulette selection Roulette selection selects individuals with a probability in proportion to their fitness. This is the most general method in EAs; however, procedures such as scaling are necessary to perform searches efficiently. 1: GTYPE and PTYPE. 15 16 Agent-Based Modeling and Simulation with Swarm • Tournament selection Tournament selection is widely used in EAs. The selection rate in roulette selection is determined by the absolute value of fitness.

Hence, the system represents another example of this new form of human– computer interaction. 19: An example rock rhythm generated by CONGA. Undertaking similar research, the author has developed a rhythm generation system named CONGA [120]. A characteristic feature of this system is that the search technique is based on a combination of a GA and GP. First, short rhythms with a length of one bar are represented as the character sequences of genes, which are then used by a GA. Conversely, GP uses functions such as “repeat” to arrange the rhythms represented by GA units, with the aim of realizing a musical structure.

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Agent-based modeling and simulation with Swarm by Hitoshi Iba

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