2,280 research outputs found
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
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond
This study proposes the GOOSE algorithm as a novel metaheuristic algorithm
based on the goose's behavior during rest and foraging. The goose stands on one
leg and keeps his balance to guard and protect other individuals in the flock.
The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions,
and the results are verified by a comparative study with genetic algorithm
(GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness
dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10
modern benchmark functions, and the gained results are compared with three
recent algorithms, such as the dragonfly algorithm, whale optimization
algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm
is tested on 5 classical benchmark functions, and the obtained results are
evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX
optimizer, butterfly optimization algorithm (BOA), whale optimization
algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The
achieved findings attest to the proposed algorithm's superior performance
compared to the other algorithms that were utilized in the current study. The
technique is then used to optimize Welded beam design and Economic Load
Dispatch Problem, three renowned real-world engineering challenges, and the
Pathological IgG Fraction in the Nervous System. The outcomes of the
engineering case studies illustrate how well the suggested approach can
optimize issues that arise in the real-world
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