A self-adaptive harmony PSO search algorithm and its performance analysis

Zhao, Fuqing; Liu, Yang; Zhang, Chuck; Wang, Junbiao

doi:10.1016/j.eswa.2015.05.035

Cited by 59 publications

(27 citation statements)

References 48 publications

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“…The searching ability of PSO is influence by random variables 1 and 2, when we set the parameters , 1 , and 2 as fixed values [15]. We introduced the Lévy flight for the change of random step length.…”

Section: Pso -Cs Algorithmmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

Ding

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

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Section: Pso -Cs Algorithmmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

Ding

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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“…The main novelty was that the proposed model could both predict the value and capture the prevailing trend in the electricity price time series with good interpretability and accuracy. Researchers have simulated a series of evolutionary algorithms [26], such as the GA [27], simulated annealing (SA) [28], PSO [29], ant colony algorithm (ACA) [30], and other types of algorithms. GA and PSO are the most commonly used evolutionary algorithms, and PSO has been proven to show better performance on smaller network structures than GA [31].…”

Section: Intelligent Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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“…It employs a new improvisation scheme and uses a parameter adjustment strategy to generate a new solution with a learning period. Combining the harmony search algorithm with particle swarm optimization (PSO) [5], Valian et al [6] presented an intelligent global harmony search algorithm (IGHS) which has excellent performance compared with its competitors. To enhance the search efficiency and effectiveness, a self-adaptive global-best harmony search algorithm [7] is developed.…”

Section: Introductionmentioning

confidence: 99%

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

Zhao

Hao

et al. 2020

Applied Sciences

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The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.

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A self-adaptive harmony PSO search algorithm and its performance analysis

Cited by 59 publications

References 48 publications

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

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