Online control of enumeration strategies via bat algorithm and black hole optimization

Soto, Ricardo; Crawford, Broderick; Olivares, Rodrigo; Niklander, Stefanie; Johnson, Franklin; Paredes, Fernando; Olguín, Eduardo

doi:10.1007/s11047-016-9576-z

Cited by 18 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm is inspired from the black hole phenomenon [ 51 ]. In this method, at each iteration, the best candidate is chosen as the black hole and the other normal candidates are chosen as normal stars.…”

Section: Swarm Intelligence Computing Techniquesmentioning

confidence: 99%

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

Prabhakar

Rajaguru²,

Kim

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area. For general cognitive activity analysis, electroencephalography (EEG) signals are widely used as a low-resolution diagnosis tool. The EEG signals are a great boon to understand the abnormality of the brain disorders, especially schizophrenia. In this work, schizophrenia EEG signal classification is performed wherein, initially, features such as Detrend Fluctuation Analysis (DFA), Hurst Exponent, Recurrence Quantification Analysis (RQA), Sample Entropy, Fractal Dimension (FD), Kolmogorov Complexity, Hjorth exponent, Lempel Ziv Complexity (LZC), and Largest Lyapunov Exponent (LLE) are extracted initially. The extracted features are, then, optimized for selecting the best features through four types of optimization algorithms here such as Artificial Flora (AF) optimization, Glowworm Search (GS) optimization, Black Hole (BH) optimization, and Monkey Search (MS) optimization, and finally, it is classified through certain classifiers. The best results show that, for normal cases, a classification accuracy of 87.54% is obtained when BH optimization is utilized with Support Vector Machine-Radial Basis Function (SVM-RBF) kernel, and for schizophrenia cases, a classification accuracy of 92.17% is obtained when BH optimization is utilized with SVM-RBF kernel.

show abstract

Section: Swarm Intelligence Computing Techniquesmentioning

confidence: 99%

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

Prabhakar

Rajaguru²,

Kim

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…For that, we use autonomous search, which is a particular case of adaptive systems that improve their solving performance by modifying and adjusting themselves to the problem at hand, either by adaptation or supervised adaptation [22,50]. This approach has successfully been applied in constraint programming using bio-inspired algorithms [51] for controlling the process resolution of solver tools [52]. The objective of autonomous search is to allow the metaheuristic to self-adapt the value of the parameter NL during the run, according to the algorithm convergence.…”

Section: Autonomous Searchmentioning

confidence: 99%

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

show abstract

“…For that, we use autonomous search which is a particular case of adaptive systems that improve their solving performance by modifying and adjusting themselves to the problem at hand, either by adaptation or supervised adaptation (please see [3,56] for details). This approach has been successfully applied in constraint programming using bioinspired algorithms for controlling the process resolution of solver tools [57,58]. The objective of autonomous search is to allow the metaheuristic to self-adapt the value of the parameter during the run, according to the algorithm convergence.…”

Section: Adaptive Black Hole Algorithmmentioning

confidence: 99%

Adaptive Black Hole Algorithm for Solving the Set Covering Problem

Soto

Crawford

Olivares

et al. 2018

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics.

show abstract

Online control of enumeration strategies via bat algorithm and black hole optimization

Cited by 18 publications

References 24 publications

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

Adaptive Black Hole Algorithm for Solving the Set Covering Problem

Contact Info

Product

Resources

About