Publications

Cheng, J., Wang, L., Jiang, Q., & Xiong, Y. (2018). A novel cuckoo search algorithm with multiple update rules. Applied Intelligence, 48(11), 4192-4211.

Gupta, S., & Deep, K. (2018). Random walk grey wolf optimizer for constrained engineering optimization problems. Computational Intelligence, 34(4), 1025-1045.

Amirsadri, S., Mousavirad, S. J., & Ebrahimpour-Komleh, H. (2018). A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training. Neural Computing and Applications, 30(12), 3707-3720.

Babalik, A., Cinar, A. C., & Kiran, M. S. (2018). A modification of tree-seed algorithm using Deb’s rules for constrained optimization. Applied Soft Computing, 63, 289-305.

Korkmaz, S., & Kiran, M. S. (2018). An artificial algae algorithm with stigmergic behavior for binary optimization. Applied Soft Computing, 64, 627-640.

Huang, S. H., Huang, Y. H., Blazquez, C. A., & Paredes-Belmar, G. (2018). Application of the ant colony optimization in the resolution of the bridge inspection routing problem. Applied soft computing, 65, 443-461.

Araujo, L., Martinez-Romo, J., & Duque, A. (2018). Discovering taxonomies in Wikipedia by means of grammatical evolution. Soft Computing, 22(9), 2907-2919.

Ng, K. K. H., Lee, C. K., Chan, F. T., & Lv, Y. (2018). Review on meta-heuristics approaches for airside operation research. Applied Soft Computing, 66, 104-133.

Sadollah, A., Sayyaadi, H., & Yadav, A. (2018). A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing, 71, 747-782.

Noorbin, S. F. E. H., & Alfi, A. (2018). Adaptive parameter control of search group algorithm using fuzzy logic applied to networked control systems. Soft Computing, 22(23), 7939-7960.

Amiri, E., & Dehkordi, M. N. (2018). Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic. International Journal of Bio-Inspired Computation, 12(3), 164-172.

Dubey, H. M., Pandit, M., & Panigrahi, B. K. (2018). An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch. Swarm and Evolutionary Computation, 38, 12-34.

Mahdavi, S., Rahnamayan, S., & Deb, K. (2018). Opposition based learning: A literature review. Swarm and evolutionary computation, 39, 1-23.

Hu, H., Cai, Z., Hu, S., Cai, Y., Chen, J., & Huang, S. (2018). Improving monarch butterfly optimization algorithm with self-adaptive population. Algorithms, 11(5), 71.

Torabi, S., & Safi-Esfahani, F. (2018). Improved raven roosting optimization algorithm (IRRO). Swarm and Evolutionary Computation, 40, 144-154.

Bansal, J. C., Gopal, A., & Nagar, A. K. (2018). Stability analysis of artificial bee colony optimization algorithm. Swarm and evolutionary computation, 41, 9-19.

Al-Dabbagh, R. D., Neri, F., Idris, N., & Baba, M. S. (2018). Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy. Swarm and Evolutionary Computation, 43, 284-311.

Nenavath, H., Jatoth, R. K., & Das, S. (2018). A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm and Evolutionary Computation, 43, 1-30.

Nayak, C. K., & Nayak, M. R. (2018). Technoeconomic analysis of a grid-connected PV and battery energy storage system considering time of use pricing. Turkish Journal of Electrical Engineering & Computer Sciences, 26(1), 318-329.

Ibrahim, R. A., Abd Elaziz, M., & Lu, S. (2018). Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Systems with Applications, 108, 1-27.