Hybrid Harmony Search Algorithm With Grey Wolf Optimizer and Modified Opposition-Based Learning

Alomoush, Alaa A.; Alsewari, AbdulRahman A.; Alamri, Hammoudeh S.; Aloufi, Khalid S.; Zamli, Kamal Z.

doi:10.1109/access.2019.2917803

Cited by 42 publications

(17 citation statements)

References 43 publications

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“…In 2018, Gupta and Deep [39] proposed a modified version of GWO based on random walk strategy to improve the global search ability of the basic GWO algorithm. In [40], Alomoush et al presented a hybrid harmony search and GWO (CGWO) with opposition learning strategy for solving global optimization and feature selection problems. In 2016, Mittal et al [41] developed a modified version of GWO (mGWO) for global engineering optimization.…”

Section: Introductionmentioning

confidence: 99%

A Random Opposition-Based Learning Grey Wolf Optimizer

et al. 2019

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Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real world applications. In the GWO algorithm, ''C'' is an important parameter which favoring exploration. At present, the researchers are few study the parameter ''C'' in GWO algorithm. In addition, during the evolution process, the other individuals in the population move towards to the α, β, and δ wolves which are to accelerate convergence. However, GWO is easy to trap in the local optima. This paper presents a modified parameter ''C'' strategy to balance between exploration and exploitation of GWO. Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima. The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.

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Section: Introductionmentioning

confidence: 99%

A Random Opposition-Based Learning Grey Wolf Optimizer

et al. 2019

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“…The second category uses adaptive or hybridization of meta-heuristics algorithms as the search engine. Example of this category involve high-level hyper-heuristic (HHH) [48], elite-FPA [49], Learning-CS [50], Modified ABC [51], Hybrid HS with Grey Wolf Optimizer [52], Self-adaptive FPA, Hybrid ABC [53], and Improved-JA [54]. Based on the above-mentioned review, most of the existing strategies based on single meta-heuristic algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

et al. 2021

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Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.

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“…Grey wolf optimizer (GWO) based on the leadership and hunting mechanism of grey wolves is a novel meta-heuristic method for solving global optimization problems [48][49][50]. As illustrated in Fig.…”

Section: B Grey Wolf Optimizer (Gwo)mentioning

confidence: 99%

Forecasting Monthly Runoff Time Series by Single-Layer Feedforward Artificial Neural Network and Grey Wolf Optimizer

2020

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Generally, accurate hydrological forecasting information plays an increasingly important role in promoting the comprehensive benefit of hydropower reservoirs. With satisfying generalization ability and search rate, the extreme learning machine (ELM), a famous single-layer feedforward neural network, has been widely used to address regression and classification problem. However, the standard ELM method often falls into second-best solutions with a high probability due to the random assignments of network parameters. In order to overcome this problem, this paper aims at developing a hybrid model for monthly runoff time series forecasting. In the hybrid method, an effective swarm intelligence method, grey wolf optimizer (GWO), is adopted to optimize the input-hidden weights and hidden biases of the ELM method; and then the Moore-Penrose generalized inverse method is adopted to determine the hidden-output weights. The world's largest hydropower reservoir, Three Gorges, is chosen to compare the performances of various forecasting methods. Based on the simulation results, the presented method outperforms several traditional forecasting methods (like artificial neural network and support vector machine) in several quantitative indexes. Thus, a novel alternative is presented to predict the nonlinear hydrological time series in China. INDEX TERMS Hydrologic forecasting, grey wolf optimizer, extreme learning machine; artificial neural network

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Hybrid Harmony Search Algorithm With Grey Wolf Optimizer and Modified Opposition-Based Learning

Cited by 42 publications

References 43 publications

A Random Opposition-Based Learning Grey Wolf Optimizer

A Random Opposition-Based Learning Grey Wolf Optimizer

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

Forecasting Monthly Runoff Time Series by Single-Layer Feedforward Artificial Neural Network and Grey Wolf Optimizer

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