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Hao Wang, Haibin Ouyang, Liqun Gao, Wei Qin.  2014.  Opposition-based learning harmony search algorithm with mutation for solving global optimization problems. Control and Decision Conference (2014 CCDC), The 26th Chinese. :1090-1094.

This paper develops an opposition-based learning harmony search algorithm with mutation (OLHS-M) for solving global continuous optimization problems. The proposed method is different from the original harmony search (HS) in three aspects. Firstly, opposition-based learning technique is incorporated to the process of improvisation to enlarge the algorithm search space. Then, a new modified mutation strategy is instead of the original pitch adjustment operation of HS to further improve the search ability of HS. Effective self-adaptive strategy is presented to fine-tune the key control parameters (e.g. harmony memory consideration rate HMCR, and pitch adjustment rate PAR) to balance the local and global search in the evolution of the search process. Numerical results demonstrate that the proposed algorithm performs much better than the existing improved HS variants that reported in recent literature in terms of the solution quality and the stability.

Biao Zhang, Huihui Yan, Junhua Duan, Liang, J.J., Hong-yan Sang, Quan-ke Pan.  2014.  An improved harmony search algorithm with dynamic control parameters for continuous optimization problems. Control and Decision Conference (2014 CCDC), The 26th Chinese. :966-971.

An improved harmony search algorithm is presented for solving continuous optimization problems in this paper. In the proposed algorithm, an elimination principle is developed for choosing from the harmony memory, so that the harmonies with better fitness will have more opportunities to be selected in generating new harmonies. Two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process with the different search spaces of the optimization problems. Numerical results of 12 benchmark problems show that the proposed algorithm performs more effectively than the existing HS variants in finding better solutions.

M'zoughi, Fares, Bouallègue, Soufiene, Ayadi, Mounir, Garrido, Aitor J., Garrido, Izaskun.  2018.  Harmony search algorithm-based airflow control of an oscillating water column-based wave generation power plants. 2018 International Conference on Advanced Systems and Electric Technologies (IC\_ASET). :249—254.

The NEREIDA wave generation power plant installed in Mutriku, Spain is a multiple Oscillating Water Column (OWC) plant. The power takeoff consists of a Wells turbine coupled to a Doubly Fed Induction Generator (DFIG). The stalling behavior present in the Wells turbine limits the generated power. This paper presents the modeling and a Harmony Search Algorithm-based airflow control of the OWC. The Harmony Search Algorithm (HSA) is proposed to help overcome the limitations of a traditionally tuned PID. An investigation between HSA-tuned controller and the traditionally tuned controller has been performed. Results of the controlled and uncontrolled plant prove the effectiveness of the airflow control and the superiority of the HSA-tuned controller.