277 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
Iris recognition based on 2D Gabor filter
Iris recognition is a type of biometrics technology that is based on physiological features of the human body. The objective of this research is to recognize and identify iris among many irises that are stored in a visual database. This study employed a left and right iris biometric framework for inclusion decision processing by combining image processing and artificial bee colony. The proposed approach was evaluated on a visual database of 280 colored iris pictures. The database was then divided into 28 clusters. Images were preprocessed and texture features were extracted based Gabor filters to capture both local and global details within an iris. The technique begins by comparing the attributes of the online-obtained iris picture with those of the visual database. This technique either generates a reject or approve message. The consequences of the intended work reflect the output’s accuracy and integrity. This is due to the careful selection of attributes, as well as the deployment of an artificial bee colony and data clustering, which decreased complexity and eventually increased identification rate to 100%. We demonstrate that the proposed method achieves state-of-the-art performance and that our recommended procedures outperform existing iris recognition systems
How Can Bee Colony Algorithm Serve Medicine?
Healthcare professionals usually should make complex decisions
with far reaching consequences and associated risks in health
care fields. As it was demonstrated in other industries, the ability
to drill down into pertinent data to explore knowledge behind the
data can greatly facilitate superior, informed decisions to ensue
the facts. Nature has always inspired researchers to develop
models of solving the problems. Bee colony algorithm (BCA),
based on the self-organized behavior of social insects is one of
the most popular member of the family of population oriented,
nature inspired meta-heuristic swarm intelligence method
which has been proved its superiority over some other nature
inspired algorithms. The objective of this model was to identify
valid novel, potentially useful, and understandable correlations
and patterns in existing data. This review employs a thematic
analysis of online series of academic papers to outline BCA in
medical hive, reducing the response and computational time and
optimizing the problems. To illustrate the benefits of this model,
the cases of disease diagnose system are presented
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
A New K means Grey Wolf Algorithm for Engineering Problems
Purpose: The development of metaheuristic algorithms has increased by
researchers to use them extensively in the field of business, science, and
engineering. One of the common metaheuristic optimization algorithms is called
Grey Wolf Optimization (GWO). The algorithm works based on imitation of the
wolves' searching and the process of attacking grey wolves. The main purpose of
this paper to overcome the GWO problem which is trapping into local optima.
Design or Methodology or Approach: In this paper, the K-means clustering
algorithm is used to enhance the performance of the original Grey Wolf
Optimization by dividing the population into different parts. The proposed
algorithm is called K-means clustering Grey Wolf Optimization (KMGWO).
Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To
evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019
benchmark test functions. Results prove that KMGWO is better compared to GWO.
KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization
Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank
in terms of performance. Statistical results proved that KMGWO achieved a
higher significant value compared to the compared algorithms. Also, the KMGWO
is used to solve a pressure vessel design problem and it has outperformed
results.
Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also
compared to cat swarm optimization (CSO), whale optimization algorithm-bat
algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of
performance. Also, the KMGWO is used to solve a classical engineering problem
and it is superiorComment: 15 pages. World Journal of Engineering, 202
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