Optimasi Gerakan Heliks untuk Meningkatkan Performa Algoritme Alga pada Desain Pressure Vessel
Abstract
Artificial Algae Algorithm (AAA) is an optimization algorithm that takes advantage of the swarm and evolutionary models. AAA consists of three phases, which are helical movement, reproduction, and adaptation. Helical movement is a three-dimensional motion which is highly influential in the convergence rate and diversity of solutions. Optimization of helical movement aims to increase the rate of convergence by moving the algae to the best colony in the population. Best colony in population is the closest to the best light source (the target solution), so that the movement is called Best Light Movement (BLM). AAA with movement toward the best light source (AAA-BLM) is tested and implemented in the case of pressure vessel design optimization. The test results indicate that the execution time of AAA-BLM increases 1,103 times faster than AAA. The increase in speed is caused by the tournament selection of AAA which is performed before the helical movement, while the AAA-BLM is conducted if a solution after the movement is not better than previous one. In the best condition, AAA-BLM finds a solution 4,5921 times faster than AAA. In the worst condition, AAA-BLM get stuck in local optima due to helical movement is too focused on the global best which may not be the global optima.
References
Taqiyuddin dan Sasongko P.H., “Studi Optimal Power Flow pada Sistem Kelistrikan 500 kV Jawa-Bali dengan Menggunakan Particle Swarm Optimization (PSO)”, Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol. 2 No. 3, 2013.
A.S. Sinaga, “Pembebanan Ekonomis dengan Pengendalian Emisi pada Pembangkit Termis Menggunakan Algoritma Evolusi Diferensial”, Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol.3 No.2, 2014.
A.A. Aburomman & Mamun Bin Ibne R., “A novel SVM-kNN-PSO ensemble method for intrusion detection system”, Applied Soft Computing, Vol.38, hal. 360-372, Jan. 2016.
M. Ranjani & P. Murugesan, “Optimal fuzzy controller parameters using PSO for speed control of Quasi-Z Source DC/DC converter fed drive”, Applied Soft Computing, Vol.27, hal. 332-356, Feb. 2015.
Ruhul A. Sarker & Charles S. Newton, Optimization Modelling : A Practival Approach, Boca Raton, CRC Press, 2008.
Binitha S. & S.S. Sathya, “A Survey of Bio inspired Optimization Algorithms”, International Journal of Soft Computing and Engineering (IJSCE), Vol,2, Issue-2, Mei 2012.
K.T. Meetei, “A Survey: Swarm Intelligence vs. Genetic Algorithm”, International Journal of Science and Research (IJSR), hal. 231-235, 2014.
Rashmi A. Mahale & S.D.Chavan, “A Survey: Evolutionary and Swarm Based Bio-Inspired Optimization Algorithms”, International Journal of Scientific and Research Publications, Vol.2, isu 12, Des. 2012.
Xin-She Yang, “Swarm Intelligence Based Algorithms: A Critical Analysis”, Evolutionary Intelligenc, Vol.7, hal. 17-28, April 2014.
Millie Pant & Radha Thangaraj, “A New Particle Swarm Optimization with Quadratic Crossover”, International Conference of Advanced Computing and Communications (ADCOM), hal. 81–86, 2007.
Sabine Helwig, Frank Neumann, dan Rolf Wanka, Particle Swarm Optimization with Velocity Adaptation, Handbook of Swarm Intelligence Vol.8 of the series Adaptation, Learning, and Optimization, hal. 155-173, 2011.
Swagatam Das & Ajith Abraham, “Synergy of Particle Swarm Optimization with Evolutionary Algorithms for Intelligent Search and Optimization”, Proceedings of IEEE International Congress on Evolutionary Computation, Vol.1, hal. 84-88, 2006.
Kedar N.D. & Raghav P.P., “Synergy of Differential Evolution and Particle Swarm Optimization”, Proceedings of the Third International Conference on Soft Computing for Problem Solving Vol.258 of the series Advances in Intelligent Systems and Computing, hal. 143-160, 2014.
Sait Ali Uymaz, Gulay Tezel, & Esra Yel, “Artificial algae algorithm (AAA) for nonlinear global optimization”, Applied Soft Computing, Vol.31, hal. 153-171, 2015.
G.C. Onwubolu & B.V. Babu, New Optimization Techniques in Engineering, Springer, Berlin, Germany, 2004.
M. Clerc, A Method to improve Standard PSO, Open access archive HAL, France, 2009.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.