scholarly journals Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem

2020 ◽  
pp. 115-134
Author(s):  
Hamza Abubakar ◽  
Sagir Abdu M. ◽  
Surajo Yusuf ◽  
Yusuf Abdurrahman
Keyword(s):  
Hamming Distance ◽  
Search Space ◽  
Boolean Satisfiability ◽  
Computational Time ◽  
Discrete Version ◽  
Agent Based ◽  
Dragonfly Algorithm ◽  
Agent Based Modelling ◽  
Boolean Satisfiability Problem ◽  
Minimum Ratio

The development of metaheuristics and Boolean Satisfiability representation plays an important part in a neural network (NN) and Artificial Intelligence (AI) communities. In this paper, a new hybrid discrete version of the artificial dragonfly algorithm (DADA) applying a minimum objective function in agent-based modelling (ABM) obeying a specified procedure to optimize the states of neurons, for optimal Boolean Exact Satisfiability representation on NETLOGO as a dynamic platform. We combined the artificial dragonfly algorithm for its random searching ability that encourages diverse solutions and formation of static swarm’s mechanism to stimulus computational problems to converge to the best global optimal search space. The global performance of the proposed DADA was compared with genetic algorithm (GA)  that are available in the literature based on the global minimum ratio (gM), Local Minimum Ratio (yM), Computational time (CPU) and Hamming distance (HD).  The final results showed good agreement between the proposed DADA and discrete version of GA to efficiently optimize the Exact-kSAT problem.  It found that DADA-ABM has high potentiality for optimizing or modelling a network that is very hard or often impossible to capture by exact or traditional optimization modelling techniques such as Boolean satisfiability problem is better than existing methods in the literature.

scholarly journals Hybrid Genetic Algorithm Model in Neuro Symbolic Integration

2020 ◽  
Vol 9 (4) ◽  
pp. 2144-2149
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hamming Distance ◽  
Hybrid Genetic Algorithm ◽  
Hopfield Neural Network ◽  
Computational Time ◽  
Agent Based ◽  
Proposed Model ◽  
Network Studies ◽  
Minimum Ratio

The development of artificial neural network and logic programming plays an important part in neural network studies. Genetic Algorithm (GA) is one of the escorted randomly searching technicality that uses evolutional concepts of the natural election as a stimulus to solve the computational problems. The essential purposes behind the studies of the evolutional system is for developing adaptive search techniques which are robust. In this paper, GA is merged with agent based modeling (ABM) by using specified proceedings to optimise the states of neurons and energy function in the Hopfield neural network (HNN). Hence, it is observed that the GA provides the best solutions in affirming optimal states of neurons and thus, enhancing the performance of Horn Satisfiability logical program (HornSAT) in Hopfield neural network. This is due to the fact that the GA lesser susceptive to be restricted in the local optimal or in any suboptimal solutions. NETLOGO version 6.0 will be used as a dynamic platform to test our proposed model. Hence, the computer simulations will be carried out to substantiate and authenticate the efficiency of the proposed model. The results are then tabulated by evaluating the global minimum ratio, computational time and hamming distance

scholarly journals Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 716 ◽  
Author(s):  
Xiaoli Bao ◽  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Search Space ◽  
Color Image Segmentation ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Dragonfly Algorithm ◽  
Opposition Based Learning

Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

scholarly journals Agent-based Modelling and Simulation for Circular Business Model Experimentation

2021 ◽  
pp. 200055
Author(s):  
Kasper P.H. Lange ◽  
Gijsbert Korevaar ◽  
Inge F. Oskam ◽  
Igor Nikolic ◽  
Paulien M. Herder
Keyword(s):  
Business Model ◽  
Modelling And Simulation ◽  
Agent Based ◽  
Agent Based Modelling

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Skin Stem Cell Hypotheses and Long Term Clone Survival – Explored Using Agent-based Modelling

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
X. Li ◽  
A. K. Upadhyay ◽  
A. J. Bullock ◽  
T. Dicolandrea ◽  
J. Xu ◽  
...  
Keyword(s):  
Stem Cell ◽  
Agent Based ◽  
Agent Based Modelling

scholarly journals Visualising Ostia’s Processional Landscape Through a Multi-Layered Computational Approach: Case Study of the Cult of the Magna Mater

2019 ◽  
Vol 5 (1) ◽  
pp. 444-467
Author(s):  
Katherine A. Crawford
Keyword(s):  
Network Analysis ◽  
Computational Approach ◽  
Urban Network ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Existing Problems ◽  
Layered Approach ◽  
Religious Landscape ◽  
Insight Into

AbstractOstia, the ancient port of Rome, had a rich religious landscape. How processional rituals further contributed to this landscape, however, has seen little consideration. This is largely due to a lack of evidence that attests to the routes taken by processional rituals. The present study aims to address existing problems in studying processions by questioning what factors motivated processional movement routes. A novel computational approach that integrates GIS, urban network analysis, and agent-based modelling is introduced. This multi-layered approach is used to question how spectators served as attractors in the creation of a processional landscape using Ostia’s Campo della Magna Mater as a case study. The analysis of these results is subsequently used to gain new insight into how a greater processional landscape was created surrounding the sanctuary of the Magna Mater.

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