The non-monopolize search (NO): a novel single-based local search optimization algorithm
DOI:
https://doi.org/10.1007/s00521-023-09120-9الكلمات المفتاحية:
Non-monopolize search (NO) ، Local search optimization ، Metaheuristic algorithms ، Optimization problemsالملخص
Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms’ performance and help avoid local search problems.
المراجع
Yang, S., Tan, J., & Chen, B. (2022). Robust spike-based continual meta-learning improved by restricted minimum error entropy criterion. Entropy, 24(4), 455.
Yang, S., Linares-Barranco, B., & Chen, B. (2022). Heterogeneous ensemble-based spike-driven few-shot online learning. Frontiers in Neuroscience, 16, 850932.
Yang, S., Gao, T., Wang, J., Deng, B., Azghadi, M. R., Lei, T., & Linares-Barranco, B. (2022). SAM: a unified self-adaptive multicompartmental spiking neuron model for learning with working memory. Frontiers in Neuroscience, 16, 850945.
Yang, S., Wang, J., Deng, B., Azghadi, M. R., & Linares-Barranco, B. (2021). Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 7126–7140.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE.
Mitchell, M. (1998). An Introduction to Genetic Algorithms. MIT Press.
Storn, R., & Price, K. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
Abualigah, L., Yousri, D., AbdElaziz, M., Ewees, A. A., Al-Qaness, M. A., & Gandomi, H. (2021). Aquila optimizer: a novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250.
Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: a new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323.
Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advanced Engineering Software, 95, 51–67.
Rao, R. V., Savsani, V. J., & Vakharia, D. (2011). Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315.
Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513.
Bertsimas, D., & Tsitsiklis, J. (1993). Simulated annealing. Statistical Science, 8(1), 10–15.
Elaziz, M., Ewees, A. A., Yousri, D., Abualigah, L., & Al-Qaness, M. A. (2022). Modified marine predators algorithm for feature selection: case study metabolomics. Knowledge and Information Systems, 64(1), 261–287.
Yildiz, B. S., Pholdee, N., Bureerat, S., Yildiz, A. R., & Sait, S. M. (2021). Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm. Expert Systems, 38(3), e12666.
Islam, M. A., Gajpal, Y., & ElMekkawy, T. Y. (2021). Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem. Applied Soft Computing, 110, 107655.
Ibrahim, R. A., Ewees, A. A., Oliva, D., Abd Elaziz, M., & Lu, S. (2019). Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3155–3169.
Rahkar Farshi, T., & Ardabili, K. K. (2021). A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimedia Systems, 27(1), 125–142.
Khan, T. A., & Ling, S. H. (2021). A novel hybrid gravitational search particle swarm optimization algorithm. Engineering Applications of Artificial Intelligence, 102, 104263.
Al-Qaness, M. A., Ewees, A. A., & Abd Elaziz, M. (2021). Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems. Soft Computing, 25(14), 9545–9557.
Sharma, S., Saha, A. K., Majumder, A., & Nama, S. (2021). MPBOA - a novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation. Multimedia Tools and Applications, 80(8), 12035–12076.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.
Tubishat, M., Idris, N., Shuib, L., Abushariah, M. A., & Mirjalili, S. (2020). Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Systems with Applications, 145, 113122.
Liu, Z., Qin, Z., Zhu, P., & Li, H. (2020). An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems. Engineering Applications of Artificial Intelligence, 95, 103771.
Nagra, A. A., Han, F., Ling, Q. H., Abubaker, M., Ahmad, F., Mehta, S., & Apasiba, T. T. (2020). Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems. Connection Science, 32(1), 16–36.
Almabsout, E. A., El-Sehiemy, R. A., An, O. N., & Bayat, O. (2020). A hybrid local search-genetic algorithm for simultaneous placement of DG units and shunt capacitors in radial distribution systems. IEEE Access, 8, 54465–54481.
Hussien, A. G., & Amin, M. (2022). A self-adaptive Harris hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. International Journal of Machine Learning and Cybernetics, 13(2), 309–336.
Mousavirad, S. J., Ebrahimpour-Komleh, H., & Schaefer, G. (2020). Automatic clustering using a local search-based human mental search algorithm for image segmentation. Applied Soft Computing, 96, 106604.
Al-Betar, M. A. (2017). β-Hill climbing: an exploratory local search. Neural Computing and Applications, 28(1), 153–168.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.