HEURISTIC OPTIMIZATION USING GENE NAVIGATION WITH THE GRAVITATIONAL SEARCH ALGORITHM
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Jun 8, 2018
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Abstract
Automation is the effective model to reduce the human workload and to increase the accuracy of working process. It is mainly involved by utilizing the architecture of the Artificial Intelligence (AI). AI is primarily developed by using the optimization to reduce the time of the workload. Optimization is the process of identifying the best solution from the large combination of solution sets. The best solution is selected by validating the objective value of the solution set using the objective function. Without explicit programming, creating the ability of learning to the machine is known as machine learning. The machine learning required to solve the various problems raises in the power electronics application. This work mainly involved to perform the pattern matching process using the CCD sensor. And also there is need to identify the optimal position of the CCD sensor in the agriculture region. The knowledge processing exhibits the higher significance in machine learning to the pattern matching and optimal placement. Genetic optimization is the heuristic approach used in the search process, which executes the natural selection in the evolutionary process. The Gravitational Search Algorithm (GSA) is the optimization model based on the law of gravity and interaction between the mass. In this paper, unique solution is designed with the genetic algorithm by merging with the GSA to identify the optimal placement position of CCD sensor to identify the Crop disease. The performan
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[2] Beigvand, Soheil Derafshi, Hamdi Abdi, and Massimo La Scala, Combined heat and power economic dispatch problem using gravitational search algorithm, Electric Power Systems Research. 133, 2016, p.160-172.
[3] Cha, Sung-Hyuk, Tappert, Charles C, A Genetic Algorithm for Constructing Compact Binary Decision Trees, Journal of Pattern Recognition Research.4 (1) 2009, p.1-13.
[4] Doraghinejad.M, Nezamabadi-pour.H, and Mahani.A, Channel Assignment in Multi-Radio Wireless Mesh Networks Using an Improved Gravitational Search Algorithm, Journal of Network and Computer Applications, 2013, p.1–9.
[5] Kabassi, Katerina, and Maria Virvou, Combining decision-making theories with a cognitive theory for intelligent help: A comparison, IEEE Transactions on Human-Machine Systems 45, no. 2. 2015, p.176-186.
[6] Kwang Mong Sim, Sun, WH, Ant colony optimization for routing and load-balancing: survey new directions, IEEE Transactions on Systems, Man, Cybernetics-Part A: Systems and Humans, vol. 33, no. 5, 2003,p.560-572.
[7] Langley, Pat, The changing science of machine learning., Machine Learning. 82 (3) 2011, p.275–279
[8] Li.C, Li.H, and Kou.P, Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system. , Neurocomputing, vol. 124, 2013, p.139–148.
[9] Li.C, Zhou.J, Fu.B, Kou.P, and Xiao.J, T - S Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm, IEEE Transactions On Fuzzy Systems, vol. 20, no. 2, 2012, p.305–317.
[10] Mirjalili, Seyedali, and Amir H. Gandomi, Gravitational Search Algorithm With Chaos., Handbook of Neural Computation. 2017,p.1-16.
[11] Mustafa Al-Ghazal, M, El-Sayed, Kelash H, Routing optimization using Genetic Algorithm In Ad Hoc Networks,, International Symposium on Signal Processing and Information Technology, 2007,p.506-511.
[12] Nezamabadi-Pour, Hossein, and Fatemeh Barani, Gravitational Search Algorithm: Concepts, Variants, and Operators. , Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics. IGI Global, 2016, p.700-750.
[13] Rashedi.E, Nezamabadi-pour.H, and Saryazdi.S, GSA: A Gravitational Search Algorithm, Information Sciences, vol. 179, no. 13, 2009, p.2232–2248.
[14] Sheth, Amit, Internet of things to smart iot through semantic, cognitive, and perceptual computing, IEEE Intelligent Systems 31, no. 2 2016,p.108-112.
[15] Tillmann.A.M, On the Computational Intractability of Exact and Approximate Dictionary Learning. , IEEE Signal Processing Letters 22(1), 2015, p.45–49.
[16] Wu Xiao-Yan, Yang, L, Routing Optimizing Algorithm Of Mobile Ad-Hoc Network Based on Genetic Algorithm., American Journal of Engineering and Technology Research. 2011, p.835-841.
[17] Yildiz, Ali Rıza, Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm., Materials Testing 58.1 2016,p.75-78.
[18] Zhang Jun, Zhan, Zhi-hui, Lin Ying, Chen Ni, Gong Yue-jiao, Evolutionary Computation Meets Machine Learning: A Survey., Computational Intelligence Magazine. IEEE.6 (4),2011, p.68–75