6,716 research outputs found
Review of Metaheuristics and Generalized Evolutionary Walk Algorithm
Metaheuristic algorithms are often nature-inspired, and they are becoming
very powerful in solving global optimization problems. More than a dozen of
major metaheuristic algorithms have been developed over the last three decades,
and there exist even more variants and hybrid of metaheuristics. This paper
intends to provide an overview of nature-inspired metaheuristic algorithms,
from a brief history to their applications. We try to analyze the main
components of these algorithms and how and why they works. Then, we intend to
provide a unified view of metaheuristics by proposing a generalized
evolutionary walk algorithm (GEWA). Finally, we discuss some of the important
open questions.Comment: 14 page
Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms
In recent years, a plethora of new metaheuristic algorithms have explored
different sources of inspiration within the biological and natural worlds. This
nature-inspired approach to algorithm design has been widely criticised. A
notable issue is the tendency for authors to use terminology that is derived
from the domain of inspiration, rather than the broader domains of
metaheuristics and optimisation. This makes it difficult to both comprehend how
these algorithms work and understand their relationships to other
metaheuristics. This paper attempts to address this issue, at least to some
extent, by providing accessible descriptions of the most cited nature-inspired
algorithms published in the last twenty years. It also discusses commonalities
between these algorithms and more classical nature-inspired metaheuristics such
as evolutionary algorithms and particle swarm optimisation, and finishes with a
discussion of future directions for the field
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.Comment: 15 page
A Comparison of Three Recent Nature-Inspired Metaheuristics for the Set Covering Problem
The Set Covering Problem (SCP) is a classic problem in
combinatorial optimization. SCP has many applications in engineering,including problems involving routing, scheduling, stock cutting, electoral redistricting and others important real life situations. Because of its
importance, SCP has attracted attention of many researchers. However,SCP instances are known as complex and generally NP-hard problems.Due to the combinatorial nature of this problem, during the last decades,several metaheuristics have been applied to obtain efficient solutions.This paper presents a metaheuristics comparison for the SCP. Three
recent nature-inspired metaheuristics are considered: Shuffled Frog Leaping,Firefly and Fruit Fly algorithms. The results show that they can obtainn optimal or close to optimal solutions at low computational cost
One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case Study on Time Series Classification
For the last few decades, optimization has been developing at a fast rate.
Bio-inspired optimization algorithms are metaheuristics inspired by nature.
These algorithms have been applied to solve different problems in engineering,
economics, and other domains. Bio-inspired algorithms have also been applied in
different branches of information technology such as networking and software
engineering. Time series data mining is a field of information technology that
has its share of these applications too. In previous works we showed how
bio-inspired algorithms such as the genetic algorithms and differential
evolution can be used to find the locations of the breakpoints used in the
symbolic aggregate approximation of time series representation, and in another
work we showed how we can utilize the particle swarm optimization, one of the
famous bio-inspired algorithms, to set weights to the different segments in the
symbolic aggregate approximation representation. In this paper we present, in
two different approaches, a new meta optimization process that produces optimal
locations of the breakpoints in addition to optimal weights of the segments.
The experiments of time series classification task that we conducted show an
interesting example of how the overfitting phenomenon, a frequently encountered
problem in data mining which happens when the model overfits the training set,
can interfere in the optimization process and hide the superior performance of
an optimization algorithm
Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal
Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal
The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them
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