Abstract
In computational swarms, large numbers of reactive agents are simulated. The swarm individuals may coordinate their movements in a “search space” to create efficient routes, to occupy niches, or to find the highest peaks. From a more general perspective though, swarms are a means of representation and computation to bridge the gap between local, individual interactions, and global, emergent phenomena. Computational swarms bear great advantages over other numeric methods, for instance, regarding their extensibility, potential for real-time interaction, dynamic interaction topologies, close translation between natural science theory and the computational model, and the integration of multiscale and multiphysics aspects. However, the more comprehensive a swarm-based model becomes, the more demanding its configuration and the more costly its computation become. In this paper, we present an approach to effectively configure and efficiently compute swarm-based simulations by means of heuristic, population-based optimization techniques. We emphasize the commonalities of several of our recent studies that shed light on top-down model optimization and bottom-up abstraction techniques, culminating in a postulation of a general concept of self-organized optimization in swarm-based simulations.
Highlights
Agent-based modelling techniques have prepared the stage for the systematic exploration of complex systems
Swarm-based simulations are of particular interest as they are typically set up to bridge the gap between local interactions and global, emergent properties and processes (Abduction refers to the corresponding logicbased approach to infer the underlying parts of a model, whereas the field of inverse and ill-posed problems represents the mathematical, analytical analogue.) we present several approaches to optimize the local behaviours of swarm individuals in order to retrace predefined emergent phenomena
Experiments, instead of fine-tuning the parameters to optimize the ratio between agent reduction and accurate emulation, we searched for a better learning example—one that allows for the deployment of self-organizing, abstracting swarm individuals in the context of a swarm simulation
Summary
Agent-based modelling techniques have prepared the stage for the systematic exploration of complex systems. The great variability in swarms demands for special diligence to maintain computational efficiency, for instance, by reducing the search space for interacting individuals based on preceding simulation states [11]. It exalts the hardship of formulating and parameterizing the agents’ behaviours—even the execution order of location update and velocity integration in simple flocking simulations yields fundamentally different global results [12].
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