Current Science

Open Access Open Access  Restricted Access Subscription Access

Comparison of Bioinspired Computation and Optimization Techniques


Affiliations
1 Department-of-Computer Science, Government-College-University, Faisalabad - 38000, Pakistan
 

In this article we focus on the bioinspired algorithms and their computational classification. The basic ideas and various techniques developed recently are described. The research outcomes in the computational area of solution optimization are presented for different problems, i.e. mathematical, combinatorial, exact approximation and multiple objective optimization. Moreover, evolutionary, stochastic and swarm optimization algorithms are discussed. All these areas have principles of extracting natural concepts in the form of mathematics and algorithms. Nature-inspired algorithms can help explore new dimensions to solve many problems with optimal cost and time. This review shows that bioinspired computing can provide innovative optimal computational algorithms.

Keywords

Bioinspired Computing, Combinatorial Optimization, Computational Complexity, Evolutionary Algorithms.
User
Notifications
Font Size

  • Kar, A. K., Bio inspired computing – a review of algorithms and scope of applications. Expert Syst. Appl., 2016, 59, 20–32.

  • Aziz, M. S. and El Sheriff, A. Y., Biomimicry as an approach for bio-inspired structure with the aid of computation. Alexandria Eng. J., 2016, 55(1), 707–714.

  • Marinescu, D. C., Nature-inspired Algorithms and Systems, Complex Systems and Clouds, 2017, pp. 33–63.

  • Kurdi, H. et al., A combinatorial optimization algorithm for multiple cloud service composition. Comput. Electr. Eng., 2015, 42, 107–113.

  • Amiri, M. and Amiri, M., A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network. J. Theor. Biol., 2016, 410, 107–118.

  • Cordone, R. and Lulli, G., Multimode extensions of combinatorial optimization problems. Electron. Notes Discrete Math., 2016, 55, 17–20.

  • Raja, M. A. Z. et al., Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problems, J. Taiwan Inst. Chem. Eng., 2016, 60, 59–75.

  • An, H. et al., Structural optimization for multiple structure cases and multiple payload cases with a two-level multipoint approximation method. Chin. J. Aeronaut., 2016, 29(5), 1273–1284.

  • Yi, C. and Sjoden, G., Heuristic optimization of group structure using physics-based fitness approximation. Ann. Nucl. Energy, 2016, 96, 389–400.

  • Dullinger, C. and Struckl, W., Simulation-based multi-objective system optimization of train traction systems. Simul. Model. Practice Theory, 2017, 72, 104–117.

  • Vitayasak, S. and Pongcharoen, P., A tool for solving stochastic dynamic facility layout problems with stochastic demand using a genetic algorithm or modified backtracking search algorithm. Int. J. Prod. Econ., 2016, 190, 146–157.

  • Sundar, V. S. and Michael, D. S., Surrogate-enhanced stochastic search algorithms to identify implicitly defined functions for reliability analysis. Struct. Safety, 2016, 62, 1–11

  • Li, W. et al., Multi-objective evolutionary algorithms and hyperheuristics for wind farm layout optimization. Renewable Energy, 2017, 105, 473–482.

  • Jothi, R. et al., Functional grouping of similar genes using eigen analysis on minimum spanning tree based neighborhood graph. Comput. Biol. Med., 2016, 71, 135–148.

  • Shalom, M. et al., On-line maximum matching in complete multipartite graphs with an application to optical networks. Discrete Appl. Math., 2016, 199, 123–136.

  • Zhang, Z. et al., Generating combinatorial test suite using combinatorial optimization. J. Syst. Software, 2014, 98, 191–207.

  • Zhao, J. and Wang, N., A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling. Comput. Chem. Eng., 2011, 35(2), 272–283.

  • Zheng, Z. and Jiang, J., Bio-inspired coplanar-gate-coupled ITOfree oxide-based transistors employing natural nontoxic bio-polymer electrolyte. Org. Electron., 2016, 37, 474–478.

  • Sesum-Cavic, V. et al., Bio-inspired search algorithms for unstructured P2P overlay networks. Swarm Evol. Comput., 2016, 29, 73–93.

  • Maitra, A. et al., A brief survey on bio-inspired algorithms for autonomous landing. IFAC-Papers Online, 2016, 49(1), 407–412.

  • Dou, R. and Duan, H., Lévy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system. Aerosp. Sci. Technol., 2017, 61, 11–20.

  • Konar, D. and Bhattacharyya, S., An improved hybrid quantuminspired genetic algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Appl. Soft Comput., 2017, 53, 296–307.

  • Ahmed, A. M. et al., BoDMaS: Bio-inspired selfishness detection and mitigation in data management for ad-hoc social networks. Ad Hoc Networks, 2017, 55, 119–131.

  • Samanta, S. and Choudhury, A., Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding. Quantum Inspired Comput. Intell., 2017, 285–326.

  • Jia, J. et al., Joint topology control and routing for multi-radio multi-channel WMNs under SINR model using bio-inspired techniques. Appl. Soft Comput., 2015, 32, 49–58.

  • Sen, D. and Kankanhalli, M., A bio-inspired center-surround model for salience computation in images. J. Vis. Commun. Image Represent., 2015, 30, 277–288.

  • Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm intelligence: from natural to artificial systems. J. Artif. Soc. Soc. Simul., 1999, 4, 320.

  • Ismkhan, H., Effective heuristics for ant colony optimization to handle large-scale problems. Swarm Evol. Computat., 2017, 32, 140–149.

  • Luo, J. and Liu, Q., An artificial bee colony algorithm for multiobjective optimization. Appl. Soft Comput., 2017, 50, 235–251.

  • Das, S., Biswas, A., Dasgupta, S. and Abraham, A., Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Found. Comput. Intell., 2009, 3, 23–55.

  • Pradhan, P. M. and Panda, G., Solving multiobjective problems using cat swarm optimization. Expert Syst. Appl., 2012, 39(3), 2956–2964.

  • Yang, X. S. and Deb, S., Engineering optimisation by cuckoo search. Int. J. Math. Model. Num. Optim., 2010, 1(4), 330–343.

  • Chu, Y., Mi, H., Liao, H., Ji, Z. and Wu, Q. H., A fast bacterial swarming algorithm for high-dimensional function optimization. In IEEE Congress on Evolutionary Computation (CEC 2008), 2008, pp. 3135–3140.

  • Yang, X. S., Firefly algorithm, Levy flights and global optimization. Res. Dev. Intell. Syst., 2010, XXVI, 209–218.

  • Shen, W., Guo, X., Wu, C. and Wu, D., Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Syst., 2011, 24(3), 378–385.

  • Shi, Y., Particle swarm optimization, developments, applications and resources. In Proceedings of the IEEE Congress on Evolutionary Computation, 2001, vol. 1, pp. 81–86.


Abstract Views: 371

PDF Views: 101




  • Comparison of Bioinspired Computation and Optimization Techniques

Abstract Views: 371  |  PDF Views: 101

Authors

Muhammad Kashif Hanif
Department-of-Computer Science, Government-College-University, Faisalabad - 38000, Pakistan
Ramzan Talib
Department-of-Computer Science, Government-College-University, Faisalabad - 38000, Pakistan
Muhammad Awais
Department-of-Computer Science, Government-College-University, Faisalabad - 38000, Pakistan
Muhammad Yahya Saeed
Department-of-Computer Science, Government-College-University, Faisalabad - 38000, Pakistan
Umer Sarwar
Department-of-Computer Science, Government-College-University, Faisalabad - 38000, Pakistan

Abstract


In this article we focus on the bioinspired algorithms and their computational classification. The basic ideas and various techniques developed recently are described. The research outcomes in the computational area of solution optimization are presented for different problems, i.e. mathematical, combinatorial, exact approximation and multiple objective optimization. Moreover, evolutionary, stochastic and swarm optimization algorithms are discussed. All these areas have principles of extracting natural concepts in the form of mathematics and algorithms. Nature-inspired algorithms can help explore new dimensions to solve many problems with optimal cost and time. This review shows that bioinspired computing can provide innovative optimal computational algorithms.

Keywords


Bioinspired Computing, Combinatorial Optimization, Computational Complexity, Evolutionary Algorithms.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi3%2F450-453