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Leandro Passos edited this page Jan 8, 2020 · 59 revisions

LibOPT is an optimization library developed in C language for the development of metaheuristic-based techniques. Currently, we have the following techniques implemented and tested:

  • Standard

    • Particle Swarm Optimization [1]
    • Particle Swarm Optimization with Adaptive Inertia Weight [2]
    • Bat Algorithm [3]
    • Flower Pollination Algorithm [4]
    • Firefly Algorithm [5]
    • Cuckoo Search [6]
    • Genetic Programming [7]
    • Black Hole Algorithm [8]
    • Migrating Birds Optimization [9]
    • Geometric Semantic Genetic Programming [10]
    • Artificial Bee Colony [11]
    • Water Cycle Algorithm [12]
    • Harmony Search [13]
    • Improved Harmony Search [14]
    • Parameter-setting-free Harmony Search [15]
    • Brain Storm Optimization [17]
    • Lion Optimization Algorithm [18]
    • Genetic Algorithm [19]
    • Backtracking Search Optimization Algorithm [20]
    • JADE: Adaptive Differential Evolution with Optional External Archive [21]
    • Artificial Butterfly Optimization [22]
    • Simulated Annealing [23]
    • Differential Evolution [24]
    • Cartesian Genetic Programming [25]

  • Quaternion-based

    • Quaternion Harmony Search [16]
    • Quaternion Backtracking Search Optimization Algorithm [26]

If you have any question, please let us know. The white paper about LibOPT can be accessed here.

Just a quick reminder: LibOPT aims at minimizing functions. So, keep that in mind when designing your functions.

References
[1] J. Kennedy, R. C. Eberhart and Y. Shi. Swarm intelligence. Artificial Intelligence (2001).
[2] A. Nickabadi, M. M. Ebadzadeh and R. Safabakhsh. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing, vol. 11, n. 4, pp. 3658-3670 (2011).
[3] X.-S. Yang and A. H. Gandomi. Bat algorithm: A novel approach for global engineering optimization. Engineering Computations vol. 5, n. 29, pp. 464-483 (2012).
[4] X.-S. Yang. Flower pollination algorithm for global optimization. Proceedings of the 11th International Conference on Unconventional Computation and Natural Computation, pp. 240-249 (2012).
[5] X.-S. Yang and X. He. Firefly algorithm: Recent advances and applications, International Journal of Swarm Intelligence, vol. 1, n. 1, pp. 36–50 (2013).
[6] X.-S. Yang and S. Deb. Cuckoo search via lévy flight. World Congress on Nature & Biologically Inspired Computing, pp. 210-214 (2009).
[7] J. Koza. Genetic programming: On the programming of computers by means of natural selection. MIT Press (1992).
[8] A. Hatamlou. Black hole: A new heuristic optimization approach for data clustering. Information Sciences, vol. 222, pp. 175-184 (2013).
[9] E. Duman, M. Uysal, and A. F. Alkaya. Migrating birds optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences, vol. 217, pp. 65-77 (2012).
[10] A. Moraglio, K. Krawiec and C. G. Johnson. Geometric semantic genetic programming. Lecture Notes in Computer Science, vol. 7491, pp. 21-31 (2012).
[11] D. Karaboga and B. Basturk. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, vol. 39, n. 3, pp. 459-471 (2007).
[12] H. Eskandar, et al. Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, vol. 110, pp. 151-166 (2012).
[13] Z. W. Geem, J. H. Kim, and G. V. Loganathan. A new heuristic optimization algorithm: Harmony search. Simulation vol. 76, n. 2, pp. 60-68 (2001).
[14] M. Mahdavi, M. Fesanghary, and E. Damangir. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, vol. 188, n. 2, pp. 1567-1579 (2007).
[15] Z. W. Geem and K.-B. Sim. Parameter-setting-free harmony search algorithm. Applied Mathematics and Computation, vol. 217, n. 8, pp 3881-3889 (2010).
[16] J. P. Papa, D. R. Pereira, A. J. Baldassin and X.-S. Yang. On the harmony search using quaternions. Proceedings of the 7th International IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, pp. 126-137 (2016).
[17] Y.H. Shi. An Optimization Algorithm Based on Brainstorming Process. International Journal of Swarm Intelligence Research (2011).
[18] M. Yazdani and F. Jolai. Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, vol. 3, n. 1, pp 24-36 (2016).
[19] D. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning (1989).
[20] P. Civicioglu. Backtracking Search Optimization Algorithm for numerical optimization problems. Applied Mathematics and Computation, vol. 219, n. 15, pp 8121--8144 (2013).
[21] J. Zhang, A. C. Sanderson. JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, vol. 13, n. 5, pp 945--958 (2009).
[22] Qi, X. and Zhu, Y. and Zhang, H. A New Meta-Heuristic Butterfly-inspired Algorithm. Journal of Computational Science, vol. 23, n. 1, pp 226--239 (2017).
[23] S. Kirkpatrick; C. D. Gelatt; M. P. Vecchi. Optimization by Simulated Annealing. Science, New Series, Vol. 220, No. 4598. (May 13, 1983), pp. 671-680.
[24] R. Storn and K. Price. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341–359, 1997.
[25] Julian F Miller and Stephen L Smith. Redundancy and computational efficiency in cartesian genetic programming. IEEE Transactions on Evolutionary Computation, 10(2):167–174, 2006.
[26] L. A. Passos, D. Rodrigues, and J. P. Papa, “Quaternion-based backtracking search optimization algorithm,” in 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019, pp. 3014–3021.

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