Abstract
The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.
- Seid Miad Zandavi, Zexi Hu, Yuk Ying Chung, and Ali Anaissi. 2019. Augmented reality vision improving educational learning. Aust. J. Intell. Inf. Process. Syst. 15, 3 (2019), 49–58.Google Scholar
- Mahmoud Abdulwahed, Zoltan K. Nagy, Richard E. Blanchard, et al. 2008. Beyond the classroom walls: Remote labs, authentic experimentation with theory lectures. In Proceedings of the 19th Annual Conference of the Australasian Association for Engineering Education: To Industry and Beyond. Institution of Engineers, Australia, 435.Google Scholar
- V. Judson Harward, Jesus A. Del Alamo, Steven R. Lerman, Philip H. Bailey, Joel Carpenter, Kimberley DeLong, Chris Felknor, James Hardison, Bryant Harrison, Imad Jabbour, et al. 2008. The ilab shared architecture: A web services infrastructure to build communities of internet accessible laboratories. Proc. IEEE 96, 6 (2008), 931–950.Google Scholar
Cross Ref
- David Lowe, Stephen Conlon, Steve Murray, Lothar Weber, Michel De La Villefromoy, Euan Lindsay, Andrew Nafalski, Warren Nageswaran, and Tee Tang. 2012. LabShare: Towards cross-institutional laboratory sharing. In Internet Accessible Remote Laboratories: Scalable E-learning Tools for Engineering and Science Disciplines. IGI Global, 453–467.Google Scholar
- Jing Ma and Jeffrey V. Nickerson. 2006. Hands-on, simulated, and remote laboratories: A comparative literature review. ACM Comput. Surveys 38, 3 (2006), 7.Google Scholar
Digital Library
- Christophe Gravier, Jacques Fayolle, Bernard Bayard, Mikael Ates, and Jeremy Lardon. 2008. State of the art about remote laboratories paradigms-foundations of ongoing mutations. Int. J. Online Eng. 4, 1 (2008), http–www.Google Scholar
- Nawat Nuntasen and Supachate Innet. 2007. Application of genetic algorithm for solving university timetabling problems: A case study of Thai universities. UTCC Engineering Research Papers.Google Scholar
- Clemens Nothegger, Alfred Mayer, Andreas Chwatal, and Günther R. Raidl. 2012. Solving the post enrolment course timetabling problem by ant colony optimization. Ann. Oper. Res. 194, 1 (2012), 325–339.Google Scholar
Cross Ref
- Der-Fang Shiau. 2011. A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Syst. Appl. 38, 1 (2011), 235–248.Google Scholar
Digital Library
- Ioannis X. Tassopoulos and Grigorios N. Beligiannis. 2012. A hybrid particle swarm optimization-based algorithm for high school timetabling problems. Appl. Soft Comput. 12, 11 (2012), 3472–3489.Google Scholar
Digital Library
- Leena N. Ahmed, Ender Özcan, and Ahmed Kheiri. 2015. Solving high school timetabling problems worldwide using selection hyper-heuristics. Expert Syst. Appl. 42, 13 (2015), 5463–5471.Google Scholar
Digital Library
- Sener Akpinar. 2016. Hybrid large neighbourhood search algorithm for capacitated vehicle routing problem. Expert Syst. Appl. 61 (2016), 28–38.Google Scholar
Digital Library
- Jian Li, Panos M. Pardalos, Hao Sun, Jun Pei, and Yong Zhang. 2015. Iterated local search embedded adaptive neighborhood selection approach for the multi-depot vehicle routing problem with simultaneous deliveries and pickups. Expert Syst. Appl. 42, 7 (2015), 3551–3561.Google Scholar
Digital Library
- Amin Jamili, Mohammad Ali Shafia, Seyed Jafar Sadjadi, and Reza Tavakkoli-Moghaddam. 2012. Solving a periodic single-track train timetabling problem by an efficient hybrid algorithm. Eng. Appl. Artific. Intell. 25, 4 (2012), 793–800.Google Scholar
Digital Library
- Safaai Deris, Sigeru Omatu, Hiroshi Ohta, and Puteh Saad. 1999. Incorporating constraint propagation in genetic algorithm for university timetable planning. Eng. Appl. Artific. Intell. 12, 3 (1999), 241–253.Google Scholar
Cross Ref
- Seid Miad Zandavi and Vera Chung. 2019. State estimation of nonlinear dynamic system using novel heuristic filter based on genetic algorithm. Soft Comput. 23, 14 (2019), 5559–5570.Google Scholar
Digital Library
- Shu-Kai S Fan, Yun-chia Liang, and Erwie Zahara. 2004. Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Eng. Optimiz. 36, 4 (2004), 401–418.Google Scholar
Cross Ref
- Shu-Kai S Fan, Yun-Chia Liang, and Erwie Zahara. 2006. A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search. Comput. Industr. Eng. 50, 4 (2006), 401–425.Google Scholar
Digital Library
- Seid Miad Zandavi. 2017. Surface-to-air missile path planning using genetic and PSO algorithms. J. Theoret. Appl. Mech. 55, 3 (2017), 801–812.Google Scholar
Cross Ref
- Seid Miad Zandavi, Vera Yuk Ying Chung, and Ali Anaissi. 2019. Stochastic dual simplex algorithm: A novel heuristic optimization algorithm. IEEE Trans. Cybernet. (2019), 1--10.Google Scholar
- S. H. Pourtakdoust and S. M. Zandavi. 2016. A hybrid simplex non-dominated sorting genetic algorithm for multi-objective optimization. Int. J. Swarm Intell. Evolution. Comput. 5, 3 (2016), 1–11.Google Scholar
- Seid Miad Zandavi and Seid H. Pourtakdoust. 2018. Multidisciplinary design of a guided flying vehicle using simplex nondominated sorting genetic algorithm II. Struct. Multidisc. Optimiz. 57, 2 (2018), 705–720.Google Scholar
Digital Library
- Natércia Lima, Clara Viegas, and Francisco José García-Peñalvo. 2019. Different didactical approaches using a remote lab: Identification of impact factors. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 14, 3 (2019), 76–86.Google Scholar
- Clara Viegas, Ana Pavani, Natércia Lima, Arcelina Marques, Isabel Pozzo, Elsa Dobboletta, Vanessa Atencia, Daniel Barreto, Felipe Calliari, André Fidalgo, et al. 2018. Impact of a remote lab on teaching practices and student learning. Comput. Edu. 126 (2018), 201–216.Google Scholar
Cross Ref
- S. M. Zandavi and V. Chung. 2018. Augmented reality for remote laboratory improving educational learning: Using elevated particle swarm optimization in object tracking scheme. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’18). 1–6.Google Scholar
- Singiresu S. Rao. 2009. Engineering Optimization: Theory and Practice. John Wiley & Sons.Google Scholar
Cross Ref
- Hadi Nobahari, Seid Miad Zandavi, and Hamed Mohammadkarimi. 2016. Simplex filter: A novel heuristic filter for nonlinear systems state estimation. Appl. Soft Comput. 49 (2016), 474–484.Google Scholar
Digital Library
- Nidamarthi Srinivas and Kalyanmoy Deb. 1994. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolution. Comput. 2, 3 (1994), 221–248.Google Scholar
Digital Library
- Rachid Chelouah and Patrick Siarry. 2003. Genetic and nelder–mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur. J. Operation. Res. 148, 2 (2003), 335–348.Google Scholar
Cross Ref
- Rachid Chelouah and Patrick Siarry. 2000. Tabu search applied to global optimization. Eur. J. Operation. Res. 123, 2 (2000), 256–270.Google Scholar
Cross Ref
- Rachid Chelouah and Patrick Siarry. 2000. A continuous genetic algorithm designed for the global optimization of multimodal functions. J. Heurist. 6, 2 (2000), 191–213.Google Scholar
Digital Library
- Pyari Mohan Pradhan and Ganapati Panda. 2012. Solving multiobjective problems using cat swarm optimization. Expert Syst. Appl. 39, 3 (2012), 2956–2964.Google Scholar
Digital Library
- Jesus Moises Osorio Velazquez, Carlos A. Coello Coello, and Alfredo Arias-Montano. 2014. Multi-objective compact differential evolution. In Proceedings of the IEEE Symposium on Differential Evolution (SDE’14). IEEE, 1–8.Google Scholar
Cross Ref
- Hossein Hemmatian, Abdolhossein Fereidoon, and Ehsanolah Assareh. 2014. Optimization of hybrid laminated composites using the multi-objective gravitational search algorithm (MOGSA). Eng. Optimiz. 46, 9 (2014), 1169–1182.Google Scholar
Cross Ref
- Xiangui Shi and Dekui Kong. 2015. A multi-objective ant colony optimization algorithm based on elitist selection strategy.Metallurg. Mining Industry6 (2015).Google Scholar
- Emrah Hancer, Bing Xue, Mengjie Zhang, Dervis Karaboga, and Bahriye Akay. 2015. A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’15). IEEE, 2420–2427.Google Scholar
Cross Ref
Index Terms
(auto-classified)Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm
Recommendations
Task Scheduling in Multiprocessor System Using Genetic Algorithm
ICMLC '10: Proceedings of the 2010 Second International Conference on Machine Learning and ComputingThe general problem of multiprocessor scheduling can be stated as scheduling a task graph onto a multiprocessor system so that schedule length can be optimized. Task scheduling in multiprocessor system is a NP-complete problem. In literature, several ...
Genetic algorithm with genetic engineering technology for multi-objective dynamic job shop scheduling problems
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationGenetic algorithms were intensively investigated in various modifications and in combinations with other algorithms for solving the NP-hard scheduling problem. This extended abstract describes a genetic algorithm approach for solving large job shop ...
An improved genetic algorithm with conditional genetic operators and its application to set-covering problem
The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an ...






Comments