Publications

Publications

The Harmony Search algorithm (HSA) publicatons found in Web of Science are applied in about 25 major fields (e.g., civil engineering, computer science, industrial engineering, business, economy, art).

Firstly,
they can be divided into whether the HSA was cited only (Citation) or was applied (Application).
Second,
(Application) publications can be divided into whether the Original HSA was used (HS) or modified HSA was used to improve the optimization performance (Variants of HS, VoHS).
Third,
(VoHS) publications can be divided into
whether it has one objective function (Single) or two or more objective function (Multi).

1) (Single) publications were classified as HS Operator, Parameter, Population-based and Hybrid.
· HS Operator : Papers that transform operators such as HMC and PA
· Parameter : Papers that used the modified equations for determining the values of HMCR, PAR, and BW
· Population-based : Papers in which HM divided into several groups (parallel calculation, solution exchange, etc.)
· Hybrid : Papers optimized by using other optimization algorithm’s operators are combined to improve search ability

2) (Multi) publications were classified as Non-Pareto or Pareto-based.
· Non-Pareto : Papers that select the optimal solutions through weighting method, etc.
· Pareto-based : Papers that select the optimal solutions through Pareto model
No. Year Journal
Information
Major DOWNLOAD
765 2018

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

Citation /
Major :
Computer Science
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764 2018

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

Citation /
Major :
Computer Science
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763 2018

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.

Citation /
Major :
Computer Science
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762 2018

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.

Citation /
Major :
Computer Science
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761 2018

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

Citation /
Major :
Computer Science
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760 2018

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.

Citation /
Major :
Computer Science
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759 2018

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

Citation /
Major :
Computer Science
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758 2018

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.

Citation /
Major :
Computer Science
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757 2018

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.

Citation /
Major :
Computer Science
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756 2018

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.

Citation /
Major :
Computer Science
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755 2018

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.

Citation /
Major :
Computer Science
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754 2018

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.

Citation /
Major :
Computer Science
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753 2018

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

Citation /
Major :
Computer Science
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752 2018

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.

Citation /
Major :
Computer Science
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751 2018

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

Citation /
Major :
Computer Science
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750 2018

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

Citation /
Major :
Computer Science
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749 2018

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.

Citation /
Major :
Computer Science
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748 2018

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.

Citation /
Major :
Computer Science
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747 2018

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.

Citation /
Major :
Computer Science
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746 2018

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.

Citation /
Major :
Computer Science
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