GO: Group Optimization

Year 2020, , 381 - 392, 01.06.2020

Mohammad Dehghani , Zeinab Montazeri Ali Dehghani Om Parkash Malik

https://doi.org/10.35378/gujs.567472

Cited By: 17

Abstract

This article introduces a modern optimization algorithm to solve optimization problems. Group Optimization (GO) is based on concept that uses all agents to update population of algorithm. Every agent of population could to be used for population updating. For these purpose two groups is specified for any agent. One group for good agents and another group for bad agents. These groups is used for updating position of each agent. twenty-three standard benchmark test functions are evaluated using GO and then results are compared with eight other optimization method.

Keywords

Heuristic algorithms, Optimization, Group, Group optimization, Swarm-based algorithm

References

• S. Mirjalili, "Introduction to Evolutionary Single-Objective Optimisation," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 3-14.
• T. Bäck, D. B. Fogel, and Z. Michalewicz, Evolutionary computation 1: Basic algorithms and operators: CRC press, 2018.
• M. Dehghani, Z. Montazeri, A. Dehghani, N. Nouri, and A. Seifi, "BSSA: Binary spring search algorithm," in Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on, 2017, pp. 0220-0224.
• M. Dehghani, Z. Montazeri, A. Dehghani, and A. Seifi, "Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law," in Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on, 2017, pp. 0210-0214.
• M. Dehghani, Z. Montazeri, O. P. Malik, A. Ehsanifar, and A. Dehghani, "OSA: Orientation Search Algorithm," International Journal of Industrial Electronics, Control and Optimization, vol. 2, pp. 99-112, 2019.
• M. Dehghani, M. Mardaneh, Z. Montazeri, A. Ehsanifar, M. J. Ebadi, and O. M. Grechko, "SPRING SEARCH ALGORITHM FOR SIMULTANEOUS PLACEMENT OF DISTRIBUTED GENERATION AND CAPACITORS," 2018, p. 6, 2018-12-12 2018.
• M. Zeinab and N. Taher, "Optimal utilization of electrical energy from power plants based on final energy consumption using gravitational search algorithm," Электротехника и электромеханика, 2018.
• G. Bekdaş, S. M. Nigdeli, A. E. Kayabekir, and X.-S. Yang, "Optimization in Civil Engineering and Metaheuristic Algorithms: A Review of State-of-the-Art Developments," in Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering, ed: Springer, 2019, pp. 111-137.
• Z. Montazeri and T. Niknam, "Energy carriers management based on energy consumption," in Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on, 2017, pp. 0539-0543.
• M. Dehghani, Z. Montazeri, A. Ehsanifar, A. Seifi, M. Ebadi, and O. Grechko, "PLANNING OF ENERGY CARRIERS BASED ON FINAL ENERGY CONSUMPTION USING DYNAMIC PROGRAMMING AND PARTICLE SWARM OPTIMIZATION," Електротехніка і Електромеханіка, pp. 62-71, 2018.
• H. AbouEisha, T. Amin, I. Chikalov, S. Hussain, and M. Moshkov, Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining: Springer, 2019.
• I. V. Antonov, E. Mazurov, M. Borodovsky, and Y. A. Medvedeva, "Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools," Briefings in bioinformatics, 2018.
• A. Biswas, K. Mishra, S. Tiwari, and A. Misra, "Physics-inspired optimization algorithms: a survey," Journal of Optimization, vol. 2013, 2013.
• S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," science, vol. 220, pp. 671-680, 1983.
• E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, pp. 2232-2248, 2009.
• F. F. Moghaddam, R. F. Moghaddam, and M. Cheriet, "Curved space optimization: A random search based on general relativity theory," arXiv preprint arXiv:1208.2214, 2012.
• H. Du, X. Wu, and J. Zhuang, "Small-world optimization algorithm for function optimization," in International Conference on Natural Computation, 2006, pp. 264-273.
• A. Kaveh and S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, vol. 213, pp. 267-289, 2010.
• H. Shah-Hosseini, "Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation," International Journal of Computational Science and Engineering, vol. 6, pp. 132-140, 2011.
• A. Kaveh and M. Khayatazad, "A new meta-heuristic method: ray optimization," Computers & structures, vol. 112, pp. 283-294, 2012.
• M.-H. Tayarani-N and M. Akbarzadeh-T, "Magnetic optimization algorithms a new synthesis," in Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, pp. 2659-2664.
• B. Alatas, "ACROA: artificial chemical reaction optimization algorithm for global optimization," Expert Systems with Applications, vol. 38, pp. 13170-13180, 2011.
• A. Hatamlou, "Black hole: A new heuristic optimization approach for data clustering," Information sciences, vol. 222, pp. 175-184, 2013.
• N. E. Karkalos, A. P. Markopoulos, and J. P. Davim, "Evolutionary-Based Methods," in Computational Methods for Application in Industry 4.0, ed: Springer, 2019, pp. 11-31.
• S. Mirjalili, "Genetic Algorithm," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 43-55.
• R. Storn and K. Price, "Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces," Journal of global optimization, vol. 11, pp. 341-359, 1997.
• H.-G. Beyer and H.-P. Schwefel, "Evolution strategies–A comprehensive introduction," Natural computing, vol. 1, pp. 3-52, 2002.
• S. Mirjalili, "Biogeography-Based Optimisation," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 57-72.
• J. R. Koza, "Genetic programming as a means for programming computers by natural selection," Statistics and computing, vol. 4, pp. 87-112, 1994.
• S. M. Lim and K. Y. Leong, "A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems," in Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization, ed: IntechOpen, 2018.
• J. C. Bansal, "Particle Swarm Optimization," in Evolutionary and Swarm Intelligence Algorithms, ed: Springer, 2019, pp. 11-23.
• M. Dorigo and T. Stützle, "Ant colony optimization: overview and recent advances," in Handbook of metaheuristics, ed: Springer, 2019, pp. 311-351.
• X.-S. Yang, "A new metaheuristic bat-inspired algorithm," in Nature inspired cooperative strategies for optimization (NICSO 2010), ed: Springer, 2010, pp. 65-74.
• G. Dhiman and V. Kumar, "Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications," Advances in Engineering Software, vol. 114, pp. 48-70, 2017.
• X.-S. Yang and A. Hossein Gandomi, "Bat algorithm: a novel approach for global engineering optimization," Engineering Computations, vol. 29, pp. 464-483, 2012.
• D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied soft computing, vol. 8, pp. 687-697, 2008.
• A. H. Gandomi, X.-S. Yang, and A. H. Alavi, "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems," Engineering with computers, vol. 29, pp. 17-35, 2013.
• G. Dhiman and V. Kumar, "Emperor Penguin Optimizer: A Bio-inspired Algorithm for Engineering Problems," Knowledge-Based Systems, 2018.
• S. Mirjalili, "Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems," Neural Computing and Applications, vol. 27, pp. 1053-1073, 2016.
• S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
• S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application," Advances in Engineering Software, vol. 105, pp. 30-47, 2017.
• S. Mirjalili, "Particle Swarm Optimisation," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 15-31.
• R. V. Rao, V. J. Savsani, and D. Vakharia, "Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems," Computer-Aided Design, vol. 43, pp. 303-315, 2011.
• J. G. Digalakis and K. G. Margaritis, "On benchmarking functions for genetic algorithms," International journal of computer mathematics, vol. 77, pp. 481-506, 2001.
• L. Wu, Q. Liu, X. Tian, J. Zhang, and W. Xiao, "A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems," Knowledge-Based Systems, vol. 144, pp. 153-173, 2018.