Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search(HS)algorithm is highly effective in solving a wide range of real-world engineering optimization problems.However,it still has the problems such as being prone to local optima,low optimization accuracy,and low search efficiency.To address the limitations of the HS algorithm,a novel approach called the Dual-Memory Dynamic Search Harmony Search(DMDS-HS)algorithm is introduced.The main innovations of this algorithm are as follows:Firstly,a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory,creating an effective and selectable trust region to reduce approach blind searching.Furthermore,the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability.Secondly,to boost the algorithm’s convergence speed,a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy.Lastly,the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm’s search strategies,which aim to rationalize the abilities of exploration and exploitation of the algorithm.The results tested on the Computational Experiment Competition on 2017(CEC2017)test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity,freedom from local optima,and solution accuracy.In addition,applying DMDS-HS to data clustering problems,the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms,which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

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Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search

Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.

Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.