Opposition based learning: A literature review

Mahdavi, Sedigheh; Rahnamayan, Shahryar; Deb, Kalyanmoy

doi:10.1016/j.swevo.2017.09.010

Cited by 333 publications

(123 citation statements)

References 273 publications

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“…Opposition-based learning (OBL), which considers the current solution and opposite solution simultaneously to accelerate the convergence of meta-heuristic methods [30]. On the basis of probability theory, there is a 50-50 chance that the distance between the current solution and optimal solution is farther than its corresponding opposite [51].…”

Section: Dragonfly Algorithm With Opposition-based Learning (Oblda) Bmentioning

confidence: 99%

“…So far a mass of strategies have been propounded to avoid these weaknesses. Opposition-based learning (OBL) is a more remarkable and general-purpose choice among them [29], in which the better ones are selected from current individuals and its opposite solutions through comparison [30]. With advantages in increasing population diversity and accelerating the convergence, it has increasingly extensive applications in various fields.…”

Section: Introductionmentioning

confidence: 99%

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Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

2019

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Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

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Section: Dragonfly Algorithm With Opposition-based Learning (Oblda) Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

2019

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“…For the point x ∈ [a, b], the corresponding opposite point (x 1 for short) of x could be easily obtained by x 1 = a + b − x. Based on previous references, when the optimal objective function value was unknown, the opposite directions of the current solution could increase the probability of finding better solutions [56][57][58]. The opposition learning strategy could enlarge the search region in the three-dimensional space.…”

Section: Opposition Learning Strategy To Improve the Convergence Speementioning

confidence: 99%

Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Feng

Liu

Jiang

et al. 2019

Water

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In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

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“…In 2012, Xu et al [75] provided summative research on the basic concepts, theories, and applications of OBL in intelligent algorithms, typical fields, and a number of challenges since 2005. Mahdavi et al [76] offered a global overview of research works on theories, developments, and applications of OBL from 2012 to 2018.…”

Section: Search Mechanism Of Toblmentioning

confidence: 99%

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Liu

Miao³

et al. 2019

Mathematical Problems in Engineering

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The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.

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Opposition based learning: A literature review

Cited by 333 publications

References 273 publications

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

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