2,490 research outputs found
Free Search and Particle Swarm Optimisation applied to Non-constrained Test
This article presents an evaluation of Particle Swarm Optimisation (PSO) with variable inertia weight and Free Search (FS) with variable neighbour space applied to nonconstrained numerical test. The objectives are to assess how high convergence speed reflects on adaptation to various test problems and to identify possible balance between convergence speed and adaptation, which allows the algorithms to complete successfully the process of search on heterogeneous tasks with limited computational resources within a reasonable finite time and with acceptable for engineering purposes precision. Modification strategies of both algorithms are compared in terms of their ability for search space exploration. Five numerical tests are explored. Achieved experimental results are presented and analysed
Algorithms Applied to Global Optimisation – Visual Evaluation
Evaluation and assessment of various search and optimisation algorithms is subject of large research efforts. Particular interest of this study is global optimisation and presented approach is based on observation and visual evaluation of Real-Coded Genetic Algorithm, Particle Swarm Optimisation, Differential Evolution and Free Search, which are briefly described and used for experiments. 3D graphical views, generated by visualisation tool VOTASA, illustrate essential aspects of global search process such as divergence, convergence, dependence on initialisation and utilisation of accidental events. Discussion on potential benefits of visual analysis, supported with numerical results, which could be used for comparative assessment of other methods and directions for further research conclude presented study
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Adaptive intelligence: essential aspects
The article discusses essential aspects of Adaptive Intelligence. Experimental results on optimisation of global test functions by Free Search, Differential Evolution, and Particle Swarm Optimisation clarify how these methods can adapt to multi-modal landscape
and space dominated by sub-optimal regions, without supervisors’ control. The achieved results are compared and analysed
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
Two-Stage Eagle Strategy with Differential Evolution
Efficiency of an optimization process is largely determined by the search
algorithm and its fundamental characteristics. In a given optimization, a
single type of algorithm is used in most applications. In this paper, we will
investigate the Eagle Strategy recently developed for global optimization,
which uses a two-stage strategy by combing two different algorithms to improve
the overall search efficiency. We will discuss this strategy with differential
evolution and then evaluate their performance by solving real-world
optimization problems such as pressure vessel and speed reducer design. Results
suggest that we can reduce the computing effort by a factor of up to 10 in many
applications
Differential Evolution for Many-Particle Adaptive Quantum Metrology
We devise powerful algorithms based on differential evolution for adaptive
many-particle quantum metrology. Our new approach delivers adaptive quantum
metrology policies for feedback control that are orders-of-magnitude more
efficient and surpass the few-dozen-particle limitation arising in methods
based on particle-swarm optimization. We apply our method to the
binary-decision-tree model for quantum-enhanced phase estimation as well as to
a new problem: a decision tree for adaptive estimation of the unknown bias of a
quantum coin in a quantum walk and show how this latter case can be realized
experimentally.Comment: Fig. 2(a) is the cover of Physical Review Letters Vol. 110 Issue 2
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