An efficient equilibrium optimizer with mutation strategy for numerical optimization

Gupta, Shubham; Deep, Kusum; Mirjalili, Seyedali

doi:10.1016/j.asoc.2020.106542

Cited by 86 publications

(35 citation statements)

References 40 publications

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“…In this subsection, 33 classical benchmark functions from reference [40] are selected, and the basic information regarding these functions is shown in Tables 1, 2 and 3. Among them, F1-F10 are single-peaked functions that have only one global optimal solution in the global range and can test the convergence efficiency and exploration capability of each algorithm.…”

Section: Benchmark Functionsmentioning

confidence: 99%

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Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Duan

Liu

et al. 2021

Engineering with Computers

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The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

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Section: Benchmark Functionsmentioning

confidence: 99%

“…There are many cases in real-world engineering that are essentially optimization problems [40]. In this subsection, MGEHO is used to determine solutions to real engineering problems and compared with other algorithms to check the applicability of the proposed algorithm.…”

Section: Applications Of Mgeho On Engineering Problemsmentioning

confidence: 99%

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Duan

Liu

et al. 2021

Engineering with Computers

View full text Add to dashboard Cite

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“…Additional exploration strategy can be applied to improve the search efficiency of the optimization algorithms and enhance the diversity of candidate particles 34 . In this IEO, the new population obtained by the EO with competitive mechanism is sorted based on the non‐dominated sorting and improved crowding distance methods.…”

Section: Proposed Moeode With Constraints Handing For Wind‐thermal Deed Problemmentioning

confidence: 99%

A new multi‐objective hybrid optimization algorithm for wind‐thermal dynamic economic emission power dispatch

Xia

Bai

2021

Int Trans Electr Energ Syst

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Summary This article presents a new optimization method to solve dynamic economic emission dispatch (DEED) problem incorporating wind power by using a hybrid nature inspired multi‐objective algorithm based on equilibrium optimizer (EO) and differential evolution (DE). In the proposed algorithm, the EO with a competitive mechanism and an additional exploration strategy is devised to explore the whole search space, while the DE with a ranking mutation operator and an opposition‐based learning strategy (OBL) is suggested to evolve the individuals of the external archive. The Kent chaotic map is adopted to generate a uniformly distributed initial population. The approach based on non‐dominated sort and improved crowding distance is utilized to screen equilibrium particles' leaders and to update the external archive. These strategies attempt to obtain a Pareto optimal front with excellent diversity and good convergence. Moreover, a real‐time constraints adjustment method and a penalty function method are combined to deal with complex constraints. The simulation results on the test system containing 10 thermal power units and one wind farm indicate that the proposed approach has much better performance than other methods for comparison.

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“…Consequently, this has led to the development of new and advanced meta-heuristic algorithms for training MLP such as the hybrid PSO-GSA [42], PSO with Autonomous Groups (PSOAG) [43], Invasive Weed Optimiser (IWO) [44], Chemical Reaction Optimiser (CRO) [45], Stochastic Fractal Search (SFS) [46], Biogeography-Based Optimizer (BBO) [47], Adaptive Best-Mass GSA (ABMGSA) [48], Chimp Optimisation Algorithm (COA) [49], Dragonfly Optimisation Algorithm (DOA) [50], Salp Swarm Optimiser (SSO) [51], Social Spider Optimisation Algorithm (SSOA), Grey Wolf Optimisation (GWO) [41], Equilibrium Optimiser (EO) [52], Sine Cosine Algorithm (SCA) [53], Modified Sine Cosine Algorithm (MSCA) [54], Whale Optimisation Algorithm (WOA) [55], Improved WOA [56], Modified WOA [57] among others.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

et al. 2021

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Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset by first, extracting and selecting the most significant statistical time-domain features. The selected features were then grouped into distinct clusters allowing for reduced computational complexity through a comparative study of four different and frequently used categories of unsupervised clustering algorithms in fault classification. The Synthetic Minority Over Sampling Technique (SMOTE) was then employed to balance the classes of the training samples from the various clusters which then served as inputs for training the supervised GWO-MLP. To validate the proposed hybrid technique (CLUST-SMOTE-GWO-MLP), it was compared with its distinct modifications (variants). The superiority of CLUST-SMOTE-GWO-MLP is demonstrated by outperforming all the distinct modifications in terms of test accuracy and seven other statistical performance evaluation metrics (error rate, sensitivity, specificity, precision, F score, Mathews Correlation Coefficient and geometric mean). The overall analysis indicates that the proposed CLUST-SMOTE-GWO-MLP is efficient and can be used to classify multiclass and imbalanced fault conditions. Article Highlights The issue of multiclass and imbalanced class outputs is addressed for improving predictive maintenance. A multiclass fault classifier based on clustering and optimised multi-layer perceptron with grey wolf is proposed. The robustness and feasibility of the proposed technique is validated on a complex hydraulic system dataset.

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An efficient equilibrium optimizer with mutation strategy for numerical optimization

Cited by 86 publications

References 40 publications

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

A new multi‐objective hybrid optimization algorithm for wind‐thermal dynamic economic emission power dispatch

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

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