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An Enhanced Grey Wolf Algorithm Based on Equalization Mechanism
Published: 30 May 2020 Publication History
ISMSI '20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Qualifiers research-article Research Refereed limited
S. Mirjalili, S. M. Mirjalili, and A. Lewis. 2014. "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69 (Mar 2014), pp. 46--61. DOI=https://doi.org/10.1016/j.advengsoft.2013.12.007 Google Scholar Digital Library A. K. M. Khairuzzaman, and S. Chaudhury. 2017. "Multilevel thresholding using grey wolf optimizer for image segmentation," Expert Systems with Applications, vol. 86 (Nov 2017), pp. 64--76. DOI=https://doi.org/10.1016/j.eswa.2017.04.029 Google Scholar Cross Ref S.S. Devi, and Y. Radhika. 2018. "A survey on machine learning and statistical techniques in bankruptcy prediction," International Journal of Machine Learning and Computing, vol. 8, no. 2(2018), pp. 133--139. DOI=https://doi.org/10.18178/ijmlc.2018.8.2.676 Google Scholar Cross Ref M. R. Mosavi, M. Khishe, and A. Ghamgosar. 2016. "Classification of sonar data set using neural network trained by Gray Wolf Optimization," Neural Network World, vol. 26, no. 4(2016), pp. 393. DOI=https://doi.org/10.14311/NNW.2016.26.023 Google Scholar Cross Ref A. Noshadi, J. Shi, W. S. Lee, P. Shi, and A. Kalam. 2016. "Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system," Neural computing and applications, vol. 27, no. 7(2016), pp. 2031--2046. DOI=https://doi.org/10.1007/s00521-015-1996-7 Google Scholar M. Naz, Z. Iqbal, N. Javaid, Z. Khan, W. Abdul, A. Almogren, and A. Alamri. 2018. "Efficient power scheduling in smart homes using hybrid grey wolf differential evolution optimization technique with real time and critical peak pricing schemes," Energies, vol. 11, no. 2(2018), pp. 384. DOI=https://doi.org/10.3390/en11020384 Google Scholar Cross Ref J. Oliveira, P. M. Oliveira, J. Boaventura-Cunha, and T. Pinho. 2017. "Chaos-based grey wolf optimizer for higher order sliding mode position control of a robotic manipulator," Nonlinear Dynamics, vol. 90, no. 2(2017), pp. 1353--1362. DOI=https://doi.org/10.1007/s11071-017-3731-7 Google Scholar Cross Ref N. Mittal, U. Singh, and B. S. Sohi. 2016. "Modified grey wolf optimizer for global engineering optimization," Applied Computational Intelligence and Soft Computing, vol. 2016(2016), pp. 8. DOI=https://doi.org/10.1155/2016/7950348 Google Scholar Digital Library M. A. Tawhid, and A. F. Ali. 2017. "A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function," Memetic Computing, vol. 9, no. 4(2017), pp. 347--359. DOI=https://doi.org/10.1007/s12293-017-0234-5 Google Scholar Cross Ref M. Wolf. 1999. "Applications of statistical mechanics in number theory," Physica A: Statistical Mechanics and its Applications, vol. 274, no. 1-2(1999), pp. 149--157. DOI= https://doi.org/10.1016/S0378-4371(99)00318-0 Google Scholar
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Since the GWO (Grey wolf optimization) has some limitation in application to real-wold problems, such as slow convergence speed, low precision and it easily falls into the local minimal in the later stage of complex optimization problems, a novel grey wolf algorithm based on equalization mechanism (EmGWO) is proposed. In the proposed algorithm, the uniform distribution point set, equalization mechanism, and winning mechanism are used to enhance the searching ability of the grey wolf algorithm. Simulation based on well-known benchmark functions demonstrates the efficiency of the proposed EmGWO.
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Home Conferences ISMSI Proceedings ISMSI '20
ISMSI '20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Association for Computing Machinery New York NY United States
ISMSI '20: 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Thimphu
Bhutan
March 21 - 22, 2020
ISMSI '20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
SESSION: Machine Learning and Intelligent Computing
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ISMSI: Intelligent Systems, Metaheuristics & Swarm Intelligence
Since the last several months, the entire world is suddenly experiencing an unprecedented tumultuous time due to the outbreak of the covid-19 pandemic. The same had brought about a remarkable period of change, adaptation & perseverance for all of us. We at IICCI was no exception and had to withstand and negotiate sudden turbulence towards the preparations of the ISMSI20 conference. We deeply appreciate the understanding, co operation & patience of the authors of the conference at that extraordinary phase even when we could not come up with proper and rational response to numerous queries regarding the fate of the conference and its ultimate organization.
No single machine learning algorithm is most accurate for all problems due to the effect of an algorithm's inductive bias. Research has shown that a combination of experts of the same type, referred to as a mixture of homogeneous experts, can increase ...
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Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices -...
This research work focusses on the optimization of a robotic manipulation problem. The problem is modeled with the robot simulation software V-REP. The objectives are the optimization movement path of the robot and its robotic arm for certain positions ...
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In this paper, we present an analysis of microscopic behaviors of ants to understand ant interactions that lead to jam-free ant traffic. For the analysis here, we use an agent-based model of ant traffic and mathematical analysis of key scenarios on the ...
Electroencephalogram (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings. It is also used to diagnose sleep disorders, ...
Studies on standard many-objective optimisation problems have indicated that multi-objective optimisation algorithms struggle to solve optimisation problems with more than three objectives, because many solutions become dominated. Therefore, the ...
In this paper, a hybrid of genetic algorithm (GA) and simulated annealing (SA) algorithm (HGSA) is developed to optimize the retardance in citrate (citric acid, CA) coated ferrofluids (FFs). The HGSA not only can overcome the deficiency of GA but also ...
Many optimisation problems have more than three objectives, referred to as many-objective optimisation problems (MaOPs). As the number of objectives increases, the number of solutions that are non-dominated with regards to one another also increases. ...
Real-world networks are often extremely polarized, because the communication between groups of vertices can be weak and, most of the time, only vertices in the same groups or sharing the same beliefs communicate to each other. We formulate the Minimum-...
This paper describes an approach for solving a tardiness constrained flow shop with simultaneously loaded stations using a Genetic Algorithm (GA). This industrial based problem is modeled from a filter basket production line and is generally solved ...
We consider a linear programming problem with uncertain input coefficients. The only information we have are lower and upper bounds for the uncertain values. This gives rise to the so called interval linear programming. The challenging problem here is ...
Cuckoo search is a bio-inspired algorithm based on the reproduction behavior of some cuckoo species. This metaheuristics seems promising to solve the capacitated vehicle routing problem. This paper analyzes the standard capacitated vehicle routing ...
The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are ...
Tabu Search (TS) metaheuristic improves simple local search algorithms (e.g. steepest ascend hill-climbing) by enabling the algorithm to escape local optima points. It has shown to be useful for addressing several combinatorial optimization problems. ...
Since the GWO (Grey wolf optimization) has some limitation in application to real-wold problems, such as slow convergence speed, low precision and it easily falls into the local minimal in the later stage of complex optimization problems, a novel grey ...
Collaborative Filtering algorithm is widely used in plentiful personal recommendation system. However, it has low accuracy prediction in sparse data set. Current mainstream collaborative filtering algorithm filter neighbor of target user by calculating ...
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Analysis of Microscopic Behavior in Ant Traffic to Understand Jam-free Transportation
Published: 30 May 2020 Publication History
ISMSI '20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Qualifiers research-article Research Refereed limited
Ralph Beckers, Jean-Louis Deneubourg, and Simon Goss. 1992. Trails and U-turns in the selection of a path by the ant Lasius niger. Journal of theoretical biology 159, 4 (1992), 397--415. Google Scholar Cross Ref Scott Camazine, Jean-Louis Deneubourg, Nigel R Franks, James Sneyd, Eric Bonabeau, and Guy Theraula. 2003. Self-organization in biological systems. Vol. 7. Princeton University Press. Google Scholar Debashish Chowdhury, Vishwesha Guttal, Katsuhiro Nishinari, and Andreas Schadschneider. 2002. A cellular-automata model of flow in ant trails: nonmonotonic variation of speed with density. Journal of Physics A: Mathematical and General 35, 41 (2002), L573. Google Scholar Cross Ref Debashish Chowdhury, Ludger Santen, and Andreas Schadschneider. 2000. Statistical physics of vehicular traffic and some related systems. Physics Reports 329, 4-6 (2000), 199--329. Google Scholar Cross Ref Debashish Chowdhury, Andreas Schadschneider, and Katsuhiro Nishinari. 2005. Physics of transport and traffic phenomena in biology: from molecular motors and cells to organisms. Physics of Life reviews 2, 4 (2005), 318--352. Google Scholar Iain D Couzin and Nigel R Franks. 2003. Self-organized lane formation and optimized traffic flow in army ants. Proceedings of the Royal Society of London B: Biological Sciences 270, 1511 (2003), 139--146. Google Scholar Cross Ref Simon Garnier, Aurélie Guérécheau, Maud Combe, Vincent Fourcassié, and Guy Theraulaz. 2009. Path selection and foraging efficiency in Argentine ant transport networks. Behavioral Ecology and Sociobiology 63, 8 (2009), 1167--1179. Google Scholar Cross Ref Simon Goss, Serge Aron, Jean-Louis Deneubourg, and Jacques Marie Pasteels. 1989. Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76, 12 (1989), 579--581. Google Scholar Dirk Helbing. 2001. Traffic and related self-driven many-particle systems. Reviews of modern physics 73, 4 (2001), 1067. Google Scholar Bert Hölldobler, Edward O Wilson, et al. 1990. The ants. Harvard University Press. Google Scholar Alexander John, Andreas Schadschneider, Debashish Chowdhury, and Katsuhiro Nishinari. 2004. Collective effects in traffic on bi-directional ant t
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