An Agent-Based Approach to Synthesizing Structures

2015 ◽  
Author(s):  
Robin Kiff ◽  
Matthew Campbell
Keyword(s):  
Random Search ◽  
New Method ◽  
Hill Climbing ◽  
Automated Synthesis ◽  
Search Methods ◽  
Agent Based ◽  
Physics Simulation

This paper discusses a new method for the automated synthesis of structures. By creating a framework to implement the synthesis, several methods are compared for the application of building tall self-supporting towers. These towers are evaluated in a physics simulation and comprised of multiple nodes and connections. In this paper, a new agent-based method is compared to existing search methods including random search, A*, and Hill-Climbing search. With the agents making local changes to nodes in the tower, the method achieves better results with less time and memory.

scholarly journals What interventions can decrease or increase belief polarisation in a population of rational agents?

2021 ◽  
Author(s):  
Piers Howe ◽  
Andrew Perfors ◽  
Keith Ransom
Keyword(s):  
New Method ◽  
Agent Based ◽  
Rational Agents ◽  
Echo Chambers ◽  
Agent Based Simulations

In many situations where people communicate (e.g., Twitter, Facebook etc), people self-organise into ‘echo chambers’ of like-minded individuals, with different echo chambers espousing very different beliefs. Why does this occur? Previous work has demonstrated that such belief polarisation can emerge even when all agents are completely rational, as long as their initial beliefs are heterogeneous and they do not automatically know who to trust. In this work, we used agent-based simulations to further investigate the mechanisms for belief polarisation. Our work extended previous work by using a more realistic scenario. In this scenario, we found that previously proposed methods for reducing belief polarisation did not work but we were able to find a new method that did. However, this same method could be reversed by adversarial entities to increase belief polarisation. We discuss how this danger can be best mitigated and what theoretical conclusions be drawn from our findings.

Random Search Methods

2010 ◽  
pp. 55-66 ◽  
Author(s):  
William W. Tipton ◽  
Richard G. Hennig
Keyword(s):  
Random Search ◽  
Search Methods

A note on continuous search algorithms

1987 ◽  
Vol 24 (1) ◽  
pp. 277-280
Author(s):  
L. E. Garey ◽  
R. D. Gupta
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Search Methods ◽  
Average Complexity

Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.

Random Search Methods

2006 ◽  
pp. 2175-2180
Author(s):  
H. Edwin Romeijn
Keyword(s):  
Random Search ◽  
Search Methods

scholarly journals One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3030
Author(s):  
Raúl Mencía ◽  
Carlos Mencía
Keyword(s):  
Local Search ◽  
Electric Vehicles ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Hill Climbing ◽  
Search Methods ◽  
Local Search Methods ◽  
Local Search Procedure ◽  
One Machine Scheduling ◽  
One Machine

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

Comparison of Selected Optimization Methods Used for Discrete Event Simulation Models and Testing Functions

2013 ◽  
Vol 816-817 ◽  
pp. 629-633
Author(s):  
Pavel Raska ◽  
Ulrych Zdenek
Keyword(s):  
Discrete Event Simulation ◽  
Evaluation Method ◽  
Random Search ◽  
Discrete Event ◽  
Simulation Models ◽  
Optimization Methods ◽  
Global Optimum ◽  
Simulation Software ◽  
Hill Climbing ◽  
Event Simulation

The paper deals with the comparison of selected optimization methods - Random Search, Hill Climbing, Tabu Search, Local Search, Downhill Simplex, Simulated Annealing, Differential Evolution and Evolution Strategy-used to search for the global optimum of the objective function specified for each simulation model. These optimization methods have to be modified in such a way that they are applicable for discrete event simulation optimization purposes. Three discrete event simulation models were built (using ARENA simulation software) which reflect real industrial systems. Then the optimization methods were tested on four testing functions. The evaluation method which uses information from the box plot characteristics was specified.

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