Ameliorated Ensemble Strategy-Based Evolutionary Algorithm with Dynamic Resources Allocations

Mashwani, Wali Khan; Shah, Syed Nouman Ali; Belhaouari, Samir Brahim; Hamdi, Abdelouahed

doi:10.2991/ijcis.d.201215.005

Cited by 14 publications

(3 citation statements)

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“…In addition, when updating the model, K-RVEA will filter the solution to limit the size of the training set, to reduce the modeling time. CSEA [ 42 ]: it uses the artificial neural network to predict the dominance relationship between candidate solutions and reference solutions. The uncertainty information in prediction is considered together with the dominance relationship to select promising solutions using the real objective functions.…”

Section: Experiments and Simulation Analysismentioning

confidence: 99%

Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm

Bao

Fang

Ding

2022

Computational Intelligence and Neuroscience

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Although the integrated model has good convergence ability, it is difficult to solve the multimodal problem and noisy problem due to the lack of uncertainty evaluation. Radial basis function model performs best for different degrees of nonlinear problems with small-scale and noisy training datasets but is insensitive to the increase of decision-space dimension, while Gaussian process regression model can provide prediction fitness and uncertainty evaluation. Therefore, an adaptive weighted strategy based integrated surrogate models is proposed to solve noisy multiobjective evolutionary problems. Based on the indicator-based multiobjective evolutionary framework, our proposed algorithm introduces the weighted combination of radial basis function and Gaussian process regression, and U-learning sampling scheme is adopted to improve the performance of population in convergence and diversity and judge the improvement of convergence and diversity. Finally, the effectiveness of the proposed algorithm is verified by 12 benchmark test problems, which are applied to the hybrid optimization problem on the construction of samples and the determination of parameters. The experimental results show that our proposed method is feasible and effective.

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Section: Experiments and Simulation Analysismentioning

confidence: 99%

Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm

Bao

Fang

Ding

2022

Computational Intelligence and Neuroscience

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“…(2) Integrating the advantages of different swarm intelligence algorithms to form hybrid algorithms. For example, some studies [4,[18][19][20][21] combine two or more algorithms to improve the efficiency of solving optimization problems. (3) Improving the existing algorithms; the commonly used ones include chaotic maps [22,23], cellular [9,24], and mutation operators [25,26].…”

Section: Introductionmentioning

confidence: 99%

Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

Zhang

Yang

2022

Scientific Programming

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As the convergence accuracy of the Porcellio scaber algorithm (PSA) is low, this study proposes an improved algorithm based on the t-distribution elite mutation mechanism. First, the improved algorithm applies a t-distribution mutation to each dimension of the optimal solution of each generation. Using the dominant information of the optimal solution and t-distribution characteristics, the result of the mutation is then employed as the updated location of the selected porcellio; thus, the algorithm enhances the ability to jump out of local extreme values and improves the convergence speed. Second, the updated iterative rule of PSA may lead to the loss of information of the elite porcellio in the last generation. To solve this problem, the judgment mechanisms of the current and previous optimal solutions are included in the algorithm process. Finally, dynamic self-adaptive improvement is applied to the weight allocation parameters of PSA. Simulation results on 24 benchmark functions show that the improved algorithm has significant advantages in convergence accuracy, convergence speed, and stability when compared with basic PSA, PSO, GSA, and FPA. This indicates that the improved algorithm has certain advantages in terms of optimization. An optimal solution with good practicability is obtained by solving three practical engineering problems: three-bar truss, welded beam, and tension/compression-spring design.

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“…Among them, the Nature-Inspired-Algorithms (NIAs) are stochastic optimization techniques that have simulated various sophisticated systems of biological or physical organism existing in the nature [13]. ey have successfully tackled distinct benchmark functions and many real-world optimization problems [14][15][16][17][18]. In the frameworks of NIAs, integration of various operators can categorize them into Evolutionary algorithms (EA) and Swarm Intelligence (SI) approaches for global optimization [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems

Nourin

Khan

Bilal

et al. 2022

Mathematical Problems in Engineering

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Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of population evolution; then, in later iterations, the number of solutions are allocated to each constituent algorithm based on their individual performance and achievements gained by each algorithm in the previous iteration. The performance of an algorithm is determined at the end of iteration by calculating the ratio of total updated solutions to the total assigned solutions in the amalgam. The proposed strategy effectively balanced the exploration versus exploitation dilemma via compelling the parent algorithms to show continuous improvement during the whole course of the optimization process. The performance of the proposed algorithm, ANIA is evaluated on recently designed benchmark functions of large-scale global optimization problems. The approximated results found by the proposed algorithm are promising as compared to state-of-the-art evolutionary algorithms including the GWO and TLBO in terms of diversity and proximity. The proposed ANIA has tackled most of the benchmark functions efficiently in the parlance of evolutionary computing communities.

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Ameliorated Ensemble Strategy-Based Evolutionary Algorithm with Dynamic Resources Allocations

Cited by 14 publications

References 0 publications

Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm

Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm

Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems

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