72 research outputs found
SuRF: Identification of Interesting Data Regions with Surrogate Models
Several data mining tasks focus on repeatedly inspecting multidimensional data regions summarized by a statistic. The value of this statistic (e.g., region-population sizes, order moments) is used to classify the region’s interesting-ness. These regions can be naively extracted from the entire dataspace – however, this is extremely time-consuming and compute-resource demanding. This paper studies the reverse problem: analysts provide a cut-off value for a statistic of interest and in turn our proposed framework efficiently identifies multidimensional regions whose statistic exceeds (or is below) the given cut-off value (according to user’s needs). However, as data dimensions and size increase, such task inevitably becomes laborious and costly. To alleviate this cost, our solution, coined SuRF (SUrrogate Region Finder), leverages historical region evaluations to train surrogate models that learn to approximate the distribution of the statistic of interest. It then makes use of evolutionary multi-modal optimization to effectively and efficiently identify regions of interest regardless of data size and dimensionality. The accuracy, efficiency, and scalability of our approach are demonstrated with experiments using synthetic and real-world datasets and compared with other methods
Route Planning Using Nature-Inspired Algorithms
There are many different heuristic algorithms for solving combinatorial
optimization problems that are commonly described as Nature-Inspired Algorithms
(NIAs). Generally, they are inspired by some natural phenomenon, and due to
their inherent converging and stochastic nature, they are known to give optimal
results when compared to classical approaches. There are a large number of
applications of NIAs, perhaps the most popular being route planning problems in
robotics - problems that require a sequence of translation and rotation steps
from the start to the goal in an optimized manner while avoiding obstacles in
the environment. In this chapter, we will first give an overview of
Nature-Inspired Algorithms, followed by their classification and common
examples. We will then discuss how the NIAs have applied to solve the route
planning problem.Comment: This work is part of 'High-Performance Vision Intelligence'; Part of
the Studies in Computational Intelligence book series (SCI,volume 913) and
can be accessed at:
https://link.springer.com/chapter/10.1007/978-981-15-6844-2_1
A comprehensive review of swarm optimization algorithms
Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches
Introductory Review of Swarm Intelligence Techniques
With the rapid upliftment of technology, there has emerged a dire need to
fine-tune or optimize certain processes, software, models or structures, with
utmost accuracy and efficiency. Optimization algorithms are preferred over
other methods of optimization through experimentation or simulation, for their
generic problem-solving abilities and promising efficacy with the least human
intervention. In recent times, the inducement of natural phenomena into
algorithm design has immensely triggered the efficiency of optimization process
for even complex multi-dimensional, non-continuous, non-differentiable and
noisy problem search spaces. This chapter deals with the Swarm intelligence
(SI) based algorithms or Swarm Optimization Algorithms, which are a subset of
the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence
involves the collective study of individuals and their mutual interactions
leading to intelligent behavior of the swarm. The chapter presents various
population-based SI algorithms, their fundamental structures along with their
mathematical models.Comment: Submitted to Springe
Decision of Multimodal Transportation Scheme Based on Swarm Intelligence
In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies
We present an approach for designing swarm-based optimizers for the global
optimization of expensive black-box functions. In the proposed approach, the
problem of finding efficient optimizers is framed as a reinforcement learning
problem, where the goal is to find optimization policies that require a few
function evaluations to converge to the global optimum. The state of each agent
within the swarm is defined as its current position and function value within a
design space and the agents learn to take favorable actions that maximize
reward, which is based on the final value of the objective function. The
proposed approach is tested on various benchmark optimization functions and
compared to the performance of other global optimization strategies.
Furthermore, the effect of changing the number of agents, as well as the
generalization capabilities of the trained agents are investigated. The results
show superior performance compared to the other optimizers, desired scaling
when the number of agents is varied, and acceptable performance even when
applied to unseen functions. On a broader scale, the results show promise for
the rapid development of domain-specific optimizers
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